Progressive generative adversarial networks with reliable sample identification

Generative Adversarial Networks (GANs) are deep neural network architectures comprising of two neural networks, namely discriminator and generator, which contest with each other in a zero-sum game. In the past years, although original GANs and their variations have achieved impressive success, there are some challenges still remain, especially unstable training progress leading to gradient vanishing or saturation. We can show by inspection that the reliable samples with smaller errors are beneficial to achieve a better generator, while the unreliable one might disturb the training procedure. Enlightened from this observation, we introduce an indicator for each sample to indicate its reliability in this paper. Based on this, we exploit a new objective function to learn the generator/discriminator and infer the indicator for each sample simultaneously. In such a way, the unreliable samples that might result in the opposite side are discarded in training stage. Meanwhile, when the training errors become smaller, more and more samples are included in the reliable set of samples, until no more reliable one are produced. It is noteworthy that the proposed method is adapted to both the original GANs and its variations. Experiments on CIFAR-10, STL-10 and LSUN datasets demonstrate the state-of-the-art performance of the proposed framework with respect to GANs and its variations.