Image single classification method based on generative confrontation network

Abstract

The invention relates to an image single classification method based on a generative confrontation network. The image single classification method based on a generative confrontation network includesthe steps: constructing a generator in the generative confrontation network by means of a dense connection block structure; constructing a discriminator in the generative confrontation network; inputting positive sample training data, and using a gradient punishment algorithm to train the generative confrontation network; according to the classification effect of the model on the verification setduring the training process, adjusting the network parameters, and using the Early Stopping strategy to find the classification optimal iteration number of the model; and after the model training is completed, using the discriminator in the generative confrontation network to test the test set data, and determining the classification effect of the model through a classification recall index CRI determination model. The image single classification method based on a generative confrontation network can automatically generate a negative sample set, and can solve the problem that in a current single classification method, artificial construction of a negative sample data set is likely to cause over-fitting of the classifier.

Description

technical field [0001] The invention relates to the technical field of image classification methods, in particular to an image single classification method based on a generative confrontation network. Background technique [0002] Image classification is one of the most basic research topics in the field of computer vision. With the development of deep learning, supervised learning methods have made the task of classifying known image categories easier, more efficient, and more accurate. In this process, there is sufficient training data to drive the end-to-end learning process and the non-linear mapping enables the semantics of the images to be clearly represented. However, this ideal classification situation requires two prerequisites: one is that sufficient training data is required, and the other is that the corresponding labels of the training data are required, so the classification results will be limited to these known categories. [0003] The single classification ...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a single classification method that can automatically generate a negative sample set, aiming to solve th

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