A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Transformation GAN for Unsupervised Image Synthesis and Representation Learning
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Generative Adversarial Networks (GAN) have shown promising performance in image generation and unsupervised learning (USL). In most cases, however, the representations extracted from unsupervised GAN are usually unsatisfactory in other computer vision tasks. By using conditional GAN (CGAN), this problem could be solved to some extent, but the main shortcoming of conditional GAN is the necessity for labeled data. To improve both image synthesis quality and representation learning performancedoi:10.1109/cvpr42600.2020.00055 dblp:conf/cvpr/WangZQFTL20 fatcat:c2u6f5gxhvdrherrilqsagyerm