Abstract: The present disclosure relates to a network training method and device, an image processing method and device, the method comprising: performing pixel shuffling on a first image in a training set to obtain a second image, wherein the first image is an image subjected to pixel shuffling; performing, by a feature extraction network of a neural network, feature extraction on the first image to obtain a first image feature, and performing, by a feature extraction network, feature extraction on the second image to obtain a second image feature; performing, by a recognition network of the neural network, recognition on the first image feature to obtain a recognition result of the first image; and training the neural network according to the recognition result, the first image feature and the second image feature. Embodiments of the present disclosure enable improvement of recognition precision of neural networks.

Abstract: The present disclosure relates to a network training method and device, an image processing method and device, the method comprising: performing pixel shuffling on a first image in a training set to obtain a second image, wherein the first image is an image subjected to pixel shuffling; performing, by a feature extraction network of a neural network, feature extraction on the first image to obtain a first image feature, and performing, by a feature extraction network, feature extraction on the second image to obtain a second image feature; performing, by a recognition network of the neural network, recognition on the first image feature to obtain a recognition result of the first image; and training the neural network according to the recognition result, the first image feature and the second image feature. Embodiments of the present disclosure enable improvement of recognition precision of neural networks.

Abstract: The disclosure relates to a neural network searching method and device. The method comprises: acquiring a neural network library to be searched and a training data set; sorting neural networks with a number of trained cycles of a first preset value in the neural network library to be searched according to a descending order of recognition accuracy on the training data set to obtain a first neural network sequence set, and taking first M neural networks in the first neural network sequence set as a first neural network set to be trained; performing first-stage training on the first neural network set to be trained by using the training data set, wherein the number of training cycles of the first-stage training is a second preset value; and taking a neural network with a number of trained cycles of the sum of the first preset value and the second preset value in the neural network library to be searched as a target neural network. A corresponding device is also disclosed.