The invention discloses an image classification method based on reliable weight optimal transmission, and the method comprises the following steps: firstly carrying out the preprocessing of source domain data, and enabling a deep neural network to fit a sample label of a source domain sample image; marking a picture, marking a pseudo label on the target domain data sample, pairing nodes to realizepairing of associated pictures in a source domain and a target domain, and finally realizing automatic analysis through a feature extractor and a self-adaptive discriminator to classify the images. The invention provides a subspace reliability method for dynamically measuring sample inter-domain differences by utilizing space prototype information and an intra-domain structure. The method can beused as a pretreatment step of an adaptive technology in the prior art, and the efficiency is greatly improved. According to the method, the reliability of the contraction subspace is combined with the optimal transportation strategy, so that the depth characteristics are more obvious, and the robustness and effectiveness of the model are enhanced. The deep neural network works stably on various data sets, and the performance of the deep neural network is superior to that of an existing method.
Image classification method based on reliable weight optimal transmission