科研之友
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摘要
Generative adversarial networks (GANs), as a leading class of generative models, have shown remarkable capacity in producing photo-realistic images. In practice, challenges remain in learning complex representations regarding data distribution. In this paper, we propose the structural pattern classification task, which enriches the training of GANs in a self-supervised manner. We first leverage the self-attention layer added to the generator to synthesize images with different structural patterns. Three distinct feature matrices are randomly swapped before calculating the self-attention feature maps. Each pattern stands for one possible combination. Meanwhile, we annotate real images with a fixed pattern. Then, the adversarial training is coupled with an auxiliary classification task. The discriminator needs to tell the correct structural pattern of input images. This auxiliary task provides an additional perspective for the discriminator to learn valuable representations of the data distribution. Empirical studies on CIFAR-10, STL-10, and CELEB-A demonstrate the effectiveness of our proposed structural pattern classification in improving the quality and diversity of the generated images, but limitations remain on corresponding interpretability research and exploration of images with higher resolution.
