Abstract:
Generative Adversarial Networks (GAN) have shown promising performance in image synthesis 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 drawback of such models is the necessity for labeled data. To improve both image synthesis quality and representation learning performance under the unsupervised setting, in this paper, we propose a simple yet effective Transformation Generative Adversarial Networks (TrGAN). In our approach, instead of capturing the joint distribution of image-label pairs p(x,y) as in conditional GAN, we try to estimate the joint distribution of transformed image t(x) and transformation t. Specifically, given a randomly sampled transformation t, we train the discriminator to give an estimate of input transformation, while following the adversarial training scheme of the original GAN. In addition, intermediate feature matching as well as feature-transform matching methods are introduced to strengthen the regularization on the generated features. To evaluate the quality of both generated samples and extracted representations, extensive experiments are conducted on four public datasets. The experimental results on the quality of both the synthesized images and the extracted representations demonstrate the effectiveness of our method.
