WO2020232977A1 - 神经网络训练方法及装置以及图像处理方法及装置 - Google Patents
神经网络训练方法及装置以及图像处理方法及装置 Download PDFInfo
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Definitions
- the present disclosure relates to the field of computer technology, in particular to a neural network training method and device, and an image processing method and device.
- machine learning especially deep learning
- machine learning relies heavily on large-scale accurately labeled data sets.
- the present disclosure proposes a technical solution for neural network training and image processing.
- a neural network training method which includes: classifying a target image in a training set through a neural network to obtain a predicted classification result of the target image; according to the predicted classification result, the The initial category label and the corrected category label of the target image are used to train the neural network.
- the neural network includes a feature extraction network and a classification network, and the neural network includes N training states, where N is an integer greater than 1, wherein the neural network compares the training set
- Performing classification processing on the target image to obtain the predicted classification result of the target image includes: performing feature extraction on the target image through the feature extraction network of the i-th state to obtain the first feature of the i-th state of the target image.
- the i state is one of the N training states, and 0 ⁇ i ⁇ N; the first feature of the i-th state of the target image is classified by the classification network of the i-th state to obtain the first feature of the target image The predicted classification result of the i state.
- the training of the neural network according to the predicted classification result, the initial category label and the corrected category label of the target image includes: the predicted classification result according to the i-th state, the The initial category label of the target image and the correction category label of the i-th state determine the overall loss of the i-th state of the neural network; according to the overall loss of the i-th state, adjust the network parameters of the i-th state of the neural network to obtain Neural network in state i+1.
- the method further includes: performing feature extraction on multiple sample images of the k-th category in the training set through a feature extraction network of the i-th state to obtain the i-th state of the multiple sample images
- the k-th category is one of the K categories of the sample images in the training set, and K is an integer greater than 1; for the i-th state of the multiple sample images of the k-th category Perform clustering processing on the second feature of the k category to determine the class prototype feature of the i-th state of the k category; according to the class prototype feature of the i-th state of the K categories and the first feature of the i-th state of the target image , Determine the correction category label of the i-th state of the target image.
- the correction category label of the i-th state of the target image is determined based on the prototype features of the i-th state of the K categories and the first characteristic of the i-th state of the target image , Including: respectively acquiring the first feature similarity between the first feature of the i-th state of the target image and the prototype features of the i-th state of the K categories; according to the corresponding value corresponding to the maximum value of the first feature similarity
- the category to which the prototype feature belongs determines the correction category label of the i-th state of the target image.
- the class prototype feature of the i-th state of each category includes multiple class prototype features, wherein the first feature of the i-th state of the target image and the K categories of the
- the first feature similarity between the class prototype features of the i-th state includes: acquiring the second feature similarity between the first feature of the i-th state and the plurality of class prototype features of the i-th state of the k-th category Degree; according to the second feature similarity, the first feature similarity between the first feature of the i-th state and the class prototype feature of the i-th state of the k-th category is determined.
- the class prototype feature of the i-th state of the k-th category includes the class center of the second feature of the i-th state of the multiple sample images of the k-th category.
- the overall loss of the i-th state of the neural network is determined according to the predicted classification result of the i-th state, the initial category label of the target image, and the corrected category label of the i-th state, It includes: determining the first loss of the i-th state of the neural network according to the predicted classification result of the i-th state and the initial category label of the target image; according to the predicted classification result of the i-th state and the target The correction category label of the i-th state of the image determines the second loss of the i-th state of the neural network; determines the neural network according to the first loss of the i-th state and the second loss of the i-th state The overall loss of the i-th state.
- an image processing method comprising: inputting an image to be processed into a neural network for classification processing to obtain an image classification result, wherein the neural network includes training according to the above method.
- Neural network is provided.
- a neural network training device which includes: a predictive classification module for classifying target images in a training set through a neural network to obtain a predictive classification result of the target image; network training The module is used to train the neural network according to the predicted classification result, the initial category label and the corrected category label of the target image.
- the neural network includes a feature extraction network and a classification network, and the neural network includes N training states, where N is an integer greater than 1, wherein the predictive classification module includes: feature extraction A sub-module for feature extraction of the target image through the feature extraction network of the i-th state to obtain the first feature of the i-th state of the target image, where the i-th state is one of the N training states, And 0 ⁇ i ⁇ N; the result determination sub-module is used to classify the first feature of the i-th state of the target image through the i-th state classification network to obtain the predicted classification result of the i-th state of the target image .
- the predictive classification module includes: feature extraction A sub-module for feature extraction of the target image through the feature extraction network of the i-th state to obtain the first feature of the i-th state of the target image, where the i-th state is one of the N training states, And 0 ⁇ i ⁇ N; the result determination sub-module is used to classify the first feature of the i-
- the network training module includes: a loss determination module, configured to determine the target image according to the predicted classification result of the i-th state, the initial category label of the target image, and the correction category label of the i-th state The overall loss of the i-th state of the neural network; a parameter adjustment module for adjusting the network parameters of the i-th state neural network according to the overall loss of the i-th state to obtain the i+1-th state neural network.
- the device further includes: a sample feature extraction module, configured to perform feature extraction on multiple sample images of the k-th category in the training set through the feature extraction network in the i-th state to obtain the multiple The second feature of the i-th state of the sample images, the k-th category is one of the K categories of the sample images in the training set, and K is an integer greater than 1;
- the clustering module is used to compare the Perform clustering processing on the second feature of the i-th state of the multiple sample images of the k-th category to determine the class prototype feature of the i-th state of the k-th category;
- the prototype feature of the i state and the first feature of the i state of the target image determine the correction category label of the i state of the target image.
- the label determination module includes: a similarity acquisition sub-module, configured to respectively acquire the first feature of the i-th state of the target image and the prototype features of the i-th state of the K categories The first feature similarity between the two; the label determination sub-module is used to determine the correction category label of the i-th state of the target image according to the category to which the prototype feature corresponding to the maximum value of the first feature similarity belongs.
- the class prototype feature of the i-th state of each category includes multiple class prototype features, wherein the similarity acquisition sub-module is used to: obtain the first feature of the i-th state and The second feature similarity between the multiple prototype features of the i-th state of the k-th category; according to the second feature similarity, the first feature of the i-th state and the i-th category of the k-th category are determined The first feature similarity between the prototype features of the state.
- the class prototype feature of the i-th state of the k-th category includes the class center of the second feature of the i-th state of the multiple sample images of the k-th category.
- the loss determination module includes: a first loss determination submodule, configured to determine the first loss determination sub-module of the neural network according to the predicted classification result of the i-th state and the initial category label of the target image The first loss of the i state; the second loss determination sub-module is used to determine the i state of the neural network according to the predicted classification result of the i state and the correction category label of the i state of the target image The second loss; the overall loss determination sub-module, which is used to determine the overall loss of the i-th state of the neural network according to the first loss of the i-th state and the second loss of the i-th state.
- an image processing device includes: an image classification module for inputting an image to be processed into a neural network for classification processing to obtain an image classification result, wherein the neural network Including the neural network trained according to the above device.
- an electronic device including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute The above method.
- a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions implement the foregoing method when executed by a processor.
- a computer program including computer readable code, and when the computer readable code runs in an electronic device, a processor in the electronic device executes the above method.
- the training process of the neural network can be jointly monitored by the initial category label and the corrected category label of the target image, and the optimization direction of the neural network can be jointly determined, thereby simplifying the training process and network structure.
- Fig. 1 shows a flowchart of a neural network training method according to an embodiment of the present disclosure.
- Fig. 2 shows a schematic diagram of an application example of a neural network training method according to an embodiment of the present disclosure.
- Fig. 3 shows a block diagram of a neural network training device according to an embodiment of the present disclosure.
- Fig. 4 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
- Fig. 5 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
- Fig. 1 shows a flowchart of a neural network training method according to an embodiment of the present disclosure.
- the neural network training method includes:
- step S11 the target image in the training set is classified through the neural network to obtain the predicted classification result of the target image
- step S12 the neural network is trained according to the predicted classification result, the initial category label and the corrected category label of the target image.
- the neural network training method can be executed by electronic equipment such as a terminal device or a server.
- the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone,
- UE user equipment
- PDA personal digital assistant
- the method can be implemented by a processor calling computer-readable instructions stored in a memory.
- the method can be executed by a server.
- the training set may include a large number of sample images that are not accurately labeled. These sample images belong to different image categories.
- the image categories are, for example, face categories (such as faces of different customers) and animal categories (such as Cats, dogs, etc.), clothing categories (such as tops, pants, etc.). This disclosure does not limit the source of the sample image and its specific category.
- each sample image has an initial category label (noise label), which is used to label the category to which the sample image belongs.
- initial category label due to the inaccurate labeling, the initial category label of a certain number of sample images may exist error.
- the present disclosure does not limit the noise distribution of the initial category tags.
- the neural network to be trained may be, for example, a deep convolutional network, and the present disclosure does not limit the specific network type of the neural network.
- the target image in the training set can be input into the neural network to be trained for classification processing in step S11 to obtain the predicted classification result of the target image.
- the target image may be one or more of the sample images, for example, multiple sample images of the same training batch.
- the predicted classification result may include the predicted category to which the target image belongs.
- the neural network can be trained in step S12 according to the predicted classification result, the initial category label and the corrected category label of the target image.
- the correction category tag is used to correct the category of the target image.
- the network loss of the neural network can be determined according to the predicted classification result, the initial category label and the corrected category label, and the network parameters of the neural network can be adjusted inversely according to the network loss. After many adjustments, a neural network that meets the training conditions (such as network convergence) is finally obtained.
- the training process of the neural network can be jointly monitored by the initial category label and the corrected category label of the target image, and the optimization direction of the neural network can be jointly determined, thereby simplifying the training process and network structure.
- the neural network may include a feature extraction network and a classification network.
- the feature extraction network is used to extract features of the target image
- the classification network is used to classify the target image according to the extracted features to obtain the predicted classification result of the target image.
- the feature extraction network may include, for example, multiple convolutional layers
- the classification network may include, for example, a fully connected layer and a softmax layer.
- the present disclosure does not limit the specific types and number of network layers of the feature extraction network and the classification network.
- step S11 may include:
- the i-th state is one of the N training states, and 0 ⁇ i ⁇ N;
- the target image can be input into the feature extraction network of the i-th state for feature extraction, and the first feature of the i-th state of the target image can be output; the first feature of the i-th state can be input into the classification network of the i-th state for classification, The predicted classification result of the i-th state of the target image is output.
- the output result of the neural network in the i-th state can be obtained, so as to train the neural network based on the result.
- the method further includes:
- the corrected category label of the i-th state of the target image is determined.
- the sample images in the training set may include K categories, and K is an integer greater than one.
- the feature extraction network can be used as a feature extractor to extract features of sample images of various categories.
- a part of sample images for example, M sample images, M is an integer greater than 1 can be selected from the sample images of the kth category for feature extraction, In order to reduce computing costs.
- feature extraction can also be performed on all sample images of the k-th category, which is not limited in the present disclosure.
- M sample images can be randomly selected from the sample images of the k-th category, or M sample images can be selected in other ways (for example, according to parameters such as image clarity). No restrictions.
- the M sample images of the k-th category can be input into the feature extraction network of the i-th state for feature extraction, and the second feature of the i-th state of the M sample images (M ); Then, clustering can be performed on the M second features of the i-th state to determine the class prototype features of the i-th state of the k-th category.
- the M second features can be clustered by means of density peak clustering, K-means clustering, spectral clustering, etc.
- the present disclosure does not limit the manner of clustering .
- the class prototype feature of the i-th state of the k-th category includes the class center of the second feature of the i-th state of the plurality of sample images of the k-th category. That is, the cluster center of the M second feature clusters of the i-th state can be used as the class prototype feature of the i-th state of the k-th category.
- there may be multiple prototype features that is, multiple prototype features are selected from M second features.
- the second feature of the p images (p ⁇ M) with the highest density value can be selected as the prototype feature, or based on the density value and the similarity measurement between the features.
- Comprehensive considerations to select class prototype features Those skilled in the art can select prototype features according to actual conditions, which are not limited in the present disclosure.
- the prototype features of the class can be used to represent the features that should be extracted from the samples in each class for comparison with the features of the target image.
- part of the sample images can be selected from the sample images of the K categories, and the selected images can be input into the feature extraction network to obtain the second feature.
- Cluster the second features of each category to obtain the prototype features of each category that is, obtain the prototype features of the i-th state of the K categories.
- the correction category label of the i-th state of the target image can be determined based on the class prototype features of the i-th state of the K categories and the first feature of the i-th state of the target image.
- the category label of the target image can be corrected to provide additional supervision signals for training the neural network.
- the step of determining the correction category label of the i-th state of the target image according to the class prototype features of the i-th state of the K categories and the first feature of the i-th state of the target image Can include:
- the first feature similarity between the first feature of the i-th state of the target image and the prototype features of the i-th state of the K categories can be calculated respectively.
- the first feature similarity may be, for example, the cosine similarity or Euclidean distance between features, which is not limited in the present disclosure.
- the maximum value of the first feature similarity of the K categories may be determined, and the category to which the class prototype feature corresponding to the maximum value belongs is determined as the corrected category label of the i-th state of the target image. That is, the label corresponding to the category feature prototype with the greatest similarity is selected to assign a new label to the sample.
- the class label of the target image can be corrected by the class prototype feature, and the accuracy of the corrected class label can be improved; when the training of the neural network is supervised by the corrected class label, the training effect of the network can be improved.
- the class prototype feature of the i-th state of each category includes multiple class prototype features, wherein the first feature of the i-th state of the target image and the K categories of the
- the step of the first feature similarity between the prototype features of the i-th state may include:
- the first feature similarity between the first feature of the i-th state and the prototype feature of the i-th state of the k-th category is determined.
- the second feature between the first feature of the i-th state and the multiple prototype features of the i-th state of the k-th category can be calculated respectively.
- the feature similarity, and then the first feature similarity is determined according to the multiple second feature similarities.
- the average value of the similarities of multiple second features can be determined as the first feature similarity, or an appropriate similarity value can be selected from the multiple second feature similarities as the first
- the feature similarity is not limited in this disclosure.
- step S12 may include:
- the network parameters of the i-th state neural network are adjusted to obtain the i+1-th state neural network.
- the difference between the predicted classification result of the i-th state obtained in step S11 and the initial class label of the target image and the corrected class label of the i-th state can be used to calculate the first state of the neural network.
- the network parameters of the neural network of the i-th state can be adjusted according to the total loss of the N-1th state to obtain the neural network of the Nth state (network convergence) . Therefore, the neural network of the Nth state can be determined as the trained neural network, and the entire training process of the neural network can be completed.
- the training process of the neural network can be completed in multiple cycles to obtain a high-precision neural network.
- the predictive classification result of the i-th state, the initial category label of the target image, and the corrected category label of the i-th state determine the overall loss of the i-th state of the neural network Steps can include:
- the overall loss of the i-th state of the neural network is determined.
- the first loss of the i-th state of the neural network can be determined according to the difference between the predicted classification result of the i-th state and the initial category label; according to the predicted classification result of the i-th state and the corrected category label of the i-th state The difference between determines the second loss of the i-th state of the neural network.
- the first loss and the second loss may be, for example, a cross-entropy loss function, and the present disclosure does not limit the specific type of the loss function.
- the weighted sum of the first loss and the second loss may be determined as the overall loss of the neural network.
- Those skilled in the art can set the weights of the first loss and the second loss according to the actual situation, and this disclosure does not limit this.
- the total loss L total can be expressed as:
- the first loss and the second loss can be determined respectively by the initial category label and the corrected category label, and then the overall loss of the neural network can be determined, thereby realizing the common supervision of the two supervision signals and improving the network training effect.
- Fig. 2 shows a schematic diagram of an application example of a neural network training method according to an embodiment of the present disclosure. As shown in Figure 2, the application example can be divided into two parts: training phase 21 and label correction phase 22.
- the target image x may include multiple sample images of one training batch.
- the target image x can be input to the feature extraction network 211 (including multiple convolutional layers) for processing, and the target image x can be output
- the first feature input the first feature into the classification network 212 (including the fully connected layer and the softmax layer) for processing, and output the predicted classification result 213 (F( ⁇ ,x)) of the target image x; according to the predicted classification result 213 and
- the initial category label y, the first loss L(F( ⁇ ,x),y) can be determined; according to the predicted classification result 213 and the corrected category label Determinable second loss
- the weighted summation of the first loss and the second loss according to the weights 1- ⁇ and ⁇ can obtain the total loss L total .
- the feature extraction network 211 in this state can be reused, or the network parameters of the feature extraction network 211 in this state can be copied to obtain the feature extraction network 221 of the label correction stage 22.
- Randomly select M sample images 222 from the sample images of the k-th category in the training set for example, multiple sample images of the category "pants" in FIG. 2
- the feature set of the selected sample image of the k-th category is output.
- sample images can be randomly selected from all K categories of sample images, and a feature set 223 of the selected sample images including K categories can be obtained.
- the feature sets of the selected sample images of each category can be clustered separately, and the prototype features of the cluster can be selected according to the clustering results.
- the feature corresponding to the cluster center is determined as the prototype feature, or according to The preset rules select p class prototype features. In this way, the prototype features 224 of each category can be obtained.
- the target image x can be input to the feature extraction network 221 for processing, and the first feature G(x) of the target image x can be output, or the first feature obtained in the training phase 21 can be directly called. Then, the feature similarity between the first feature G(x) of the target image x and the prototype features of each category is respectively calculated; the category of the prototype feature corresponding to the maximum value of the feature similarity is determined as the target image x Calibration category label This completes the process of label correction. Calibration category label It can be input into the training phase 21 as an additional supervision signal in the training phase.
- the network parameters of the neural network can be adjusted inversely according to the total loss, thereby obtaining the neural network of the next state.
- the above-mentioned training phase and label correction phase are performed alternately until the network is trained to convergence, and a trainable neural network is obtained.
- a self-correction stage is added in the network training process to realize the re-correction of the noise data label, and the corrected label is used as a part of the supervision signal, and the original noise label is combined with the supervision network
- the training process can improve the generalization ability of neural networks after learning in inaccurately labeled data sets.
- the prototype features of multiple categories can be extracted, and the data distribution in the category can be better expressed through end-to-end self-learning
- the framework solves the current difficult problem of network training under real noise data sets, and simplifies the training process and network design.
- the embodiments according to the present disclosure can be applied to computer vision and other fields to realize model training under noisy data.
- an image processing method which includes: inputting an image to be processed into a neural network for classification processing to obtain an image classification result, wherein the neural network includes the method training described above The resulting neural network.
- the present disclosure also provides neural network training devices and image processing devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any neural network training method and image processing method provided in the present disclosure.
- the scheme and description and refer to the corresponding record in the method section will not be repeated.
- Fig. 3 shows a block diagram of a neural network training device according to an embodiment of the present disclosure.
- a neural network training device includes: a predictive classification module 31, which is used to classify target images in the training set through a neural network to obtain a predictive classification result of the target image; a network training module 32 uses Training the neural network according to the predicted classification result, the initial category label and the corrected category label of the target image.
- the neural network includes a feature extraction network and a classification network, and the neural network includes N training states, where N is an integer greater than 1, wherein the predictive classification module includes: feature extraction A sub-module for feature extraction of the target image through the feature extraction network of the i-th state to obtain the first feature of the i-th state of the target image, where the i-th state is one of the N training states, And 0 ⁇ i ⁇ N; the result determination sub-module is used to classify the first feature of the i-th state of the target image through the i-th state classification network to obtain the predicted classification result of the i-th state of the target image .
- the predictive classification module includes: feature extraction A sub-module for feature extraction of the target image through the feature extraction network of the i-th state to obtain the first feature of the i-th state of the target image, where the i-th state is one of the N training states, And 0 ⁇ i ⁇ N; the result determination sub-module is used to classify the first feature of the i-
- the network training module includes: a loss determination module, configured to determine the target image according to the predicted classification result of the i-th state, the initial category label of the target image, and the correction category label of the i-th state The overall loss of the i-th state of the neural network; a parameter adjustment module for adjusting the network parameters of the i-th state of the neural network according to the overall loss of the i-th state to obtain the i+1-th state of the neural network.
- the device further includes: a sample feature extraction module, configured to perform feature extraction on multiple sample images of the k-th category in the training set through the feature extraction network in the i-th state to obtain the multiple The second feature of the i-th state of the sample images, the k-th category is one of the K categories of the sample images in the training set, and K is an integer greater than 1;
- the clustering module is used to compare the Perform clustering processing on the second feature of the i-th state of the multiple sample images of the k-th category to determine the class prototype feature of the i-th state of the k-th category;
- the prototype feature of the i state and the first feature of the i state of the target image determine the correction category label of the i state of the target image.
- the label determination module includes: a similarity acquisition sub-module, configured to respectively acquire the first feature of the i-th state of the target image and the prototype features of the i-th state of the K categories The first feature similarity between the two; the label determination sub-module is used to determine the correction category label of the i-th state of the target image according to the category to which the prototype feature corresponding to the maximum value of the first feature similarity belongs.
- the class prototype feature of the i-th state of each category includes multiple class prototype features, wherein the similarity acquisition sub-module is used to: obtain the first feature of the i-th state and The second feature similarity between the multiple prototype features of the i-th state of the k-th category; according to the second feature similarity, the first feature of the i-th state and the i-th category of the k-th category are determined The first feature similarity between the prototype features of the state.
- the class prototype feature of the i-th state of the k-th category includes the class center of the second feature of the i-th state of the multiple sample images of the k-th category.
- the loss determination module includes: a first loss determination submodule, configured to determine the first loss determination sub-module of the neural network according to the predicted classification result of the i-th state and the initial category label of the target image The first loss of the i state; the second loss determination sub-module is used to determine the i state of the neural network according to the predicted classification result of the i state and the correction category label of the i state of the target image The second loss; the overall loss determination sub-module, which is used to determine the overall loss of the i-th state of the neural network according to the first loss of the i-th state and the second loss of the i-th state.
- an image processing device includes: an image classification module for inputting an image to be processed into a neural network for classification processing to obtain an image classification result, wherein the neural network Including the neural network trained according to the above device.
- the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
- the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
- the embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor.
- the computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium.
- An embodiment of the present disclosure also proposes an electronic device including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.
- the embodiment of the present disclosure also proposes a computer program, the computer program includes computer readable code, and when the computer readable code runs in an electronic device, a processor in the electronic device executes the above method.
- the electronic device can be provided as a terminal, server or other form of device.
- FIG. 4 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure.
- the electronic device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.
- the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.
- the processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
- the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method.
- the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components.
- the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
- the memory 804 is configured to store various types of data to support operations in the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc.
- the memory 804 can be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
- SRAM static random access memory
- EEPROM electrically erasable programmable read-only memory
- EPROM erasable Programmable Read Only Memory
- PROM Programmable Read Only Memory
- ROM Read Only Memory
- Magnetic Memory Flash Memory
- Magnetic Disk Magnetic Disk or Optical Disk.
- the power supply component 806 provides power for various components of the electronic device 800.
- the power supply component 806 may include a power management system, one or more power supplies, and other components associated with the generation, management, and distribution of power for the electronic device 800.
- the multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
- the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
- the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure related to the touch or slide operation.
- the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
- the audio component 810 is configured to output and/or input audio signals.
- the audio component 810 includes a microphone (MIC).
- the microphone is configured to receive external audio signals.
- the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
- the audio component 810 further includes a speaker for outputting audio signals.
- the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
- the peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include but are not limited to: home button, volume button, start button, and lock button.
- the sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation.
- the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components.
- the component is the display and the keypad of the electronic device 800.
- the sensor component 814 can also detect the electronic device 800 or the electronic device 800.
- the position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800.
- the sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
- the sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
- the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.
- the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
- the electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof.
- the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
- the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication.
- the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
- RFID radio frequency identification
- IrDA infrared data association
- UWB ultra-wideband
- Bluetooth Bluetooth
- the electronic device 800 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
- ASIC application specific integrated circuits
- DSP digital signal processors
- DSPD digital signal processing devices
- PLD programmable logic devices
- FPGA field A programmable gate array
- controller microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
- a non-volatile computer-readable storage medium such as a memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the foregoing method.
- FIG. 5 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.
- the electronic device 1900 may be provided as a server. 5
- the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by the memory 1932, for storing instructions executable by the processing component 1922, such as application programs.
- the application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions.
- the processing component 1922 is configured to execute instructions to perform the above-described methods.
- the electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input output (I/O) interface 1958 .
- the electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
- a non-volatile computer-readable storage medium such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the foregoing method.
- the present disclosure may be a system, method, and/or computer program product.
- the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.
- the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
- the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
- Computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
- RAM random access memory
- ROM read-only memory
- EPROM erasable programmable read-only memory
- flash memory flash memory
- SRAM static random access memory
- CD-ROM compact disk read-only memory
- DVD digital versatile disk
- memory stick floppy disk
- mechanical encoding device such as a printer with instructions stored thereon
- the computer-readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
- the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
- the network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
- the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
- the computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or in one or more programming languages.
- Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
- Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server carried out.
- the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to access the Internet connection).
- LAN local area network
- WAN wide area network
- an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions.
- the computer-readable program instructions are executed to realize various aspects of the present disclosure.
- These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
- each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more functions for implementing the specified logical function.
- Executable instructions may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
- each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
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Abstract
Description
Claims (21)
- 一种神经网络训练方法,其特征在于,包括:通过神经网络对训练集中的目标图像进行分类处理,得到所述目标图像的预测分类结果;根据所述预测分类结果、所述目标图像的初始类别标签及校正类别标签,训练所述神经网络。
- 根据权利要求1所述的方法,其特征在于,所述神经网络包括特征提取网络和分类网络,并且所述神经网络包括N个训练状态,N为大于1的整数,其中,所述通过神经网络对训练集中的目标图像进行分类处理,得到所述目标图像的预测分类结果,包括:通过第i状态的特征提取网络对目标图像进行特征提取,得到所述目标图像的第i状态的第一特征,所述第i状态为所述N个训练状态中的一个,且0≤i<N;通过第i状态的分类网络对所述目标图像的第i状态的第一特征进行分类,得到所述目标图像的第i状态的预测分类结果。
- 根据权利要求2所述的方法,其特征在于,所述根据所述预测分类结果、所述目标图像的初始类别标签及校正类别标签,训练所述神经网络,包括:根据第i状态的预测分类结果、所述目标图像的初始类别标签及第i状态的校正类别标签,确定所述神经网络的第i状态的总体损失;根据所述第i状态的总体损失,调整第i状态的神经网络的网络参数,得到第i+1状态的神经网络。
- 根据权利要求2或3所述的方法,其特征在于,所述方法还包括:通过第i状态的特征提取网络对训练集中第k个类别的多个样本图像进行特征提取,得到所述多个样本图像的第i状态的第二特征,所述第k个类别是所述训练集中的样本图像的K个类别中的一个,K为大于1的整数;对所述第k个类别的多个样本图像的第i状态的第二特征进行聚类处理,确定所述第k个类别的第i状态的类原型特征;根据K个类别的第i状态的类原型特征以及所述目标图像的第i状态的第一特征,确定所述目标图像的第i状态的校正类别标签。
- 根据权利要求4所述的方法,其特征在于,所述根据K个类别的第i状态的类原型特征以及所述目标图像的第i状态的第一特征,确定所述目标图像的第i状态的校正类别标签,包括:分别获取所述目标图像的第i状态的第一特征与K个类别的第i状态的类原型特征之间的第一特征相似度;根据与第一特征相似度的最大值对应的类原型特征所属的类别,确定所述目标图像的第i状态的校正类别标签。
- 根据权利要求5所述的方法,其特征在于,每个类别的第i状态的类原型特征包括多个类原型特征,其中,所述分别获取所述目标图像的第i状态的第一特征与K个类别的第i状态的类原型特征之间的第一特征相似度,包括:获取所述第i状态的第一特征与第k个类别的第i状态的多个类原型特征之间的第二特征相似度;根据所述第二特征相似度,确定所述第i状态的第一特征与第k个类别的第i状态的类原型特征之间的第一特征相似度。
- 根据权利要求4-6中任意一项所述的方法,其特征在于,所述第k个类别的第i状态的类原型特征包括所述第k个类别的多个样本图像的第i状态的第二特征的类中心。
- 根据权利要求3-7中任意一项所述的方法,其特征在于,所述根据第i状态的预测分类结果、所述目标图像的初始类别标签及第i状态的校正类别标签,确定所述神经网络的第i状态的总体损失,包括:根据所述第i状态的预测分类结果以及所述目标图像的初始类别标签,确定所述神经网络的第i状态的第一损失;根据所述第i状态的预测分类结果以及所述目标图像的第i状态的校正类别标签,确定所述神经网络的第i状态的第二损失;根据所述第i状态的第一损失和所述第i状态的第二损失,确定所述神经网络的第i状态的总体损失。
- 一种图像处理方法,其特征在于,所述方法包括:将待处理图像输入神经网络中进行分类处理,得到图像分类结果,其中,所述神经网络包括根据权利要求1-8中任意一项所述的方法训练得到的神经网络。
- 一种神经网络训练装置,其特征在于,包括:预测分类模块,用于通过神经网络对训练集中的目标图像进行分类处理,得到所述目标图像的预测分类结果;网络训练模块,用于根据所述预测分类结果、所述目标图像的初始类别标签及校正类别标签,训练所述神经网络。
- 根据权利要求10所述的装置,其特征在于,所述神经网络包括特征提取网络和分类网络,并且所述神经网络包括N个训练状态,N为大于1的整数,其中,所述预测分类模块包括:特征提取子模块,用于通过第i状态的特征提取网络对目标图像进行特征提取,得到所述目标图像的第i状态的第一特征,所述第i状态为所述N个训练状态中的一个,且0≤i<N;结果确定子模块,用于通过第i状态的分类网络对所述目标图像的第i状态的第一特征进行分类,得到所述目标图像的第i状态的预测分类结果。
- 根据权利要求11所述的装置,其特征在于,所述网络训练模块包括:损失确定模块,用于根据第i状态的预测分类结果、所述目标图像的初始类别标签及第i状态的校正类别标签,确定所述神经网络的第i状态的总体损失;参数调整模块,用于根据所述第i状态的总体损失,调整第i状态的神经网络的网络参数,得到第i+1状态的神经网络。
- 根据权利要求11或12所述的装置,其特征在于,所述装置还包括:样本特征提取模块,用于通过第i状态的特征提取网络对训练集中第k个类别的多个样本图像进行特征提取,得到所述多个样本图像的第i状态的第二特征,所述第k个类别是所述训练集中的样本图像的K个类别中的一个,K为大于1的整数;聚类模块,用于对所述第k个类别的多个样本图像的第i状态的第二特征进行聚类处理,确定所述第k个类别的第i状态的类原型特征;标签确定模块,用于根据K个类别的第i状态的类原型特征以及所述目标图像的第i状态的第一特征,确定所述目标图像的第i状态的校正类别标签。
- 根据权利要求13所述的装置,其特征在于,所述标签确定模块包括:相似度获取子模块,用于分别获取所述目标图像的第i状态的第一特征与K个类别的第i状态的类原型特征之间的第一特征相似度;标签确定子模块,用于根据与第一特征相似度的最大值对应的类原型特征所属的类别,确定所述目标图像的第i状态的校正类别标签。
- 根据权利要求14所述的装置,其特征在于,每个类别的第i状态的类原型特征包括多个类原型特征,其中,所述相似度获取子模块用于:获取所述第i状态的第一特征与第k个类别的第i状态的多个类原型特征之间的第二特征相似度;根据所述第二特征相似度,确定所述第i状态的第一特征与第k个类别的第i状态的类原型特征之间的第一特征相似度。
- 根据权利要求13-15中任意一项所述的装置,其特征在于,所述第k个类别的第i状态的类原型特征包括所述第k个类别的多个样本图像的第i状态的第二特征的类中心。
- 根据权利要求12-16中任意一项所述的装置,其特征在于,损失确定模块包括:第一损失确定子模块,用于根据所述第i状态的预测分类结果以及所述目标图像的初始类别标签,确定所述神经网络的第i状态的第一损失;第二损失确定子模块,用于根据所述第i状态的预测分类结果以及所述目标图像的第i状态的校正类别标签,确定所述神经网络的第i状态的第二损失;总体损失确定子模块,用于根据所述第i状态的第一损失和所述第i状态的第二损失,确定所述神经网络的第i状态的总体损失。
- 一种图像处理装置,其特征在于,所述装置包括:图像分类模块,用于将待处理图像输入神经网络中进行分类处理,得到图像分类结果,其中,所述神经网络包括根据权利要求10-17中任意一项所述的装置训练得到的神经网络。
- 一种电子设备,其特征在于,包括:处理器;用于存储处理器可执行指令的存储器;其中,所述处理器被配置为调用所述存储器存储的指令,以执行权利要求1至9中任意一项所述的方法。
- 一种计算机可读存储介质,其上存储有计算机程序指令,其特征在于,所述计算机程序指令被处理器执行时实现权利要求1至9中任意一项所述的方法。
- 一种计算机程序,其特征在于,所述计算机程序包括计算机可读代码,当所述计算机可读代码在电子设备中运行时,所述电子设备中的处理器执行用于实现权利要求1至9中的任意一项所述的方法。
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CN115563522A (zh) * | 2022-12-02 | 2023-01-03 | 湖南工商大学 | 交通数据的聚类方法、装置、设备及介质 |
CN115563522B (zh) * | 2022-12-02 | 2023-04-07 | 湖南工商大学 | 交通数据的聚类方法、装置、设备及介质 |
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US20210326708A1 (en) | 2021-10-21 |
JP2022516518A (ja) | 2022-02-28 |
CN110210535A (zh) | 2019-09-06 |
SG11202106979WA (en) | 2021-07-29 |
CN113743535A (zh) | 2021-12-03 |
TWI759722B (zh) | 2022-04-01 |
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CN113743535B (zh) | 2024-05-24 |
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