An Unsupervised Domain Adaptive Image Classification Method Based on Conditional Generative Adversarial Networks

Abstract

The invention discloses an unsupervised domain adaptive image classification method based on a conditional generative adversarial network. The method comprises the following steps: preprocessing an image data set; constructing a cross-domain conditional confrontation image generation network by adopting a cyclic consistent generation confrontation network and applying a constraint loss function; using the preprocessed image data set to train the constructed conditional adversarial image generation network; and testing the to-be-classified target image by using the trained network model to obtain a final classification result. According to the method, a conditional adversarial cross-domain image migration algorithm is adopted to carry out mutual conversion on source domain image samples andtarget domain image samples, and consistency loss function constraint is applied to classification prediction of target images before and after conversion. Meanwhile, discriminative classification tags are applied to carry out conditional adversarial learning to align joint distribution of source domain image tags and target domain image tags, so that the source domain image with the tags is applied to train the target domain image, classification of the target image is achieved, and classification precision is improved.

Description

technical field [0001] The invention belongs to the field of unsupervised domain adaptive image classification, and in particular relates to an unsupervised domain adaptive image classification method based on conditional generation confrontation network. Background technique [0002] The development of deep learning is of great significance to the improvement of feature learning and classification task performance. Training a deep network requires a large number of labeled samples, but in practical applications, the target samples to be classified often lack category labels, which makes classification training particularly difficult. Faced with this difficulty, the usual practice is to train an effective classifier from labeled source domain samples to assist in the classification of unlabeled target domain samples, but how to reduce the distribution of source domain samples and target domain samples The difference is crucial to make this classifier better adaptable to the...

AI Technical Summary

Problems solved by technology

However, although such methods improve the performance of domain adaptation to a certain extent, they are still limited for classification tasks. They only achieve cross-domain image generation by aligning sample distributions while ignoring the impact of classification labels during this period.

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