The invention discloses an image classification method based on a confrontation network generated through feature recalibration. The image classification method based on the confrontation network generated through feature recalibration is suitable for the field of
machine learning and comprises the steps that to-be-classified image data are input into a confrontation
network model for network training; a generator and a
discriminator which are constituted by a convolutional network are constructed;
random noise is initialized and input into the generator; the
random noise is subjected to multilevel
deconvolution operation in the generator through the convolutional network, and finally, generated samples are obtained; the generated samples and authentic samples are input into the
discriminator; and the input samples are subjected to
convolution and
pooling operation in the
discriminator through the convolutional network, thus a feature graph is obtained, a compressed and activated SENetmodule is imported into an intermediate layer of the convolutional network to calibrate the feature graph, thus the calibrated feature graph is obtained, global average
pooling is used, and finally,image
data classification is output. The SENet module is imported into the intermediate layer of the discriminator, the importance degree of each feature channel is automatically learned, useful features relevant to a task are extracted, features irrelevant to the task are restrained, and thus semi-
supervised learning performance is improved.