Abstract: In a feature extraction model processing method, a sample image and an inheritance parameter of the sample image are obtained. The inheritance parameter is based on feature distinctiveness of a first image feature of the sample image. The first image feature is extracted from the sample image. A second image feature extracted from the sample image is obtained. A classification result of a first classification that is based on the second image feature is obtained. A classification loss of the first classification is adjusted based on the inheritance parameter to obtain a model compatibility loss. A classification loss of a second classification is determined. Respective model parameters of a feature extraction model to be trained and a image classification model to be trained are updated based on the model compatibility loss and the classification loss of the second classification.

Abstract: The present disclosure discloses an image processing method and related device thereof. The method includes: acquiring an image to be processed; and performing a feature extraction process on the image to be processed using a target neural network so as to obtain target feature data of the image to be processed, wherein parameters of the target neural network are time average values of parameters of a first neural network which is obtained from training under supervision by a training image set and an average network, and parameters of the average network are time average values of parameters of a second neural network which is obtained from training under supervision by the training image set and the target neural network. A corresponding device is also disclosed. Feature data of image to be processed are obtained via the feature extraction process performed on the image to be processed.

Abstract: The present disclosure relates to a network optimization method and apparatus, an image processing method and apparatus, and a storage medium. The network optimization method includes: obtaining an image sample group; obtaining a first feature and a second feature of an image in the image sample group, and obtaining a first classification result by using the first feature of the image; performing feature exchange processing on an image pair in the image sample group to obtain a new image pair; obtaining a first loss value of the first classification result, a second loss value of the new image pair, and a third loss value of first features and second features of the new image pair in a preset manner; and adjusting parameters of a neural network at least according to the first loss value, the second loss value, and the third loss value until a preset requirement is met.

Abstract: The present disclosure discloses an image processing method and related device thereof. The method includes: acquiring an image to be processed; and performing a feature extraction process on the image to be processed using a target neural network so as to obtain target feature data of the image to be processed, wherein parameters of the target neural network are time average values of parameters of a first neural network which is obtained from training under supervision by a training image set and an average network, and parameters of the average network are time average values of parameters of a second neural network which is obtained from training under supervision by the training image set and the target neural network. A corresponding device is also disclosed. Feature data of image to be processed are obtained via the feature extraction process performed on the image to be processed.

Abstract: The present disclosure relates to a network optimization method and apparatus, an image processing method and apparatus, and a storage medium. The network optimization method includes: obtaining an image sample group; obtaining a first feature and a second feature of an image in the image sample group, and obtaining a first classification result by using the first feature of the image; performing feature exchange processing on an image pair in the image sample group to obtain a new image pair; obtaining a first loss value of the first classification result, a second loss value of the new image pair, and a third loss value of first features and second features of the new image pair in a preset manner; and adjusting parameters of a neural network at least according to the first loss value, the second loss value, and the third loss value until a preset requirement is met.