Semisupervised image classification by mutual learning of multiple self‐supervised models. Issue 5 (14th January 2022) - Zhang, Jian; Yang, Jianing; Yu, Jun; Fan, Jianping
Abstract: Image classification has been widely adopted by current social media applications. Compared with fully supervised classification, semisupervised classification attracts more attention because it is commonly observed that category labels are only available for a small portion of images while most images on social media platforms do not have labels. To this end, we propose a two‐stage semisupervised learning framework. In the first stage, we train two Self‐supervised Models (SSMs). One model is initialized by predicting the rotation angles of pretransformed training images and then further trained by the labeled images. The other model is initialized by making consistent predictions for the transformed images in color, shape, and quality from the same sample image, and then further trained by the labeled images. In the second stage, we fuse the two SSMs through deep mutual learning, which enhances each of the two SSMs with the complementary information provided by the other such that the correct prediction could be shared. Experimental results on CIFAR and Caltech‐256 data sets demonstrate the effect of the proposed framework.