An Improved Self-supervised GAN via Adversarial Training

By Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Ngai-Man Cheung. 2019

14 citations recorded by the source corpus.

We propose to improve unconditional Generative Adversarial Networks (GAN) by training the self-supervised learning with the adversarial process. In particular, we apply self-supervised learning via the geometric transformation on input images and assign the pseudo-labels to these transformed images. (i) In addition to the GAN task, which distinguishes data (real) versus generated (fake) samples, we train the discriminator to predict the correct pseudo-labels of real transformed samples (classification task). Importantly, we find out that simultaneously training the discriminator to classify the fake class from the pseudo-classes of real samples for the classification task will improve the discriminator and subsequently lead better guides to train generator. (ii) The generator is trained by attempting to confuse the discriminator for not only the GAN task but also the classification task. For the classification task, the generator tries to confuse the discriminator recognizing the transformation of its output as one of the real transformed classes. Especially, we exploit that when the generator creates samples that result in a similar loss (via cross-entropy) as that of the real ones, the training is more stable and the generator distribution tends to match better the data distribution. When integrating our techniques into a state-of-the-art Auto-Encoder (AE) based-GAN model, they help to significantly boost the model's performance and also establish new state-of-the-art Frechet Inception Distance (FID) scores in the literature of unconditional GAN for CIFAR-10 and STL-10 datasets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning (2020, background): “Consequently, recent years have seen efforts [4]–[10] to mitigate these GAN problems, including using gradient matching [5] and a two time-scale update rule [7].”
Training GANs with Stronger Augmentations via Contrastive Discriminator (2021, methodology): “Self-supervised learning, including contrastive learning, have been recently applied in GAN as an auxiliary task upon the GAN loss (Chen et al., 2019; Tran et al., 2019; Lee et al., 2021; Zhao et al., 2020d), mainly in attempt to alleviate catastopic forgetting in discriminators (Chen et al., 2019).”
On Data Augmentation for GAN Training (2020, background): “[50], [51], [52] train GAN with auxiliary self-supervised tasks via multi pseudo-classes [53] that enhance stability of the optimization process.”
Hausdorff GAN: Improving GAN Generation Quality With Hausdorff Metric (2021, background): “T HE generative adversarial network (GAN) [14] has had considerable success as a generative model, and it is widely applied to a variety of real-world applications [1], [26], [31], [42].”