Interpretable Generative Adversarial Networks

Visualization of feature maps in interpretable GANs.

Abstract

Learning a disentangled representation is still a challenge in the field of the interpretability of generative adversarial networks (GANs). This paper proposes a generic method to modify a traditional GAN into an interpretable GAN, which ensures that filters in an intermediate layer of the generator encode disentangled localized visual concepts. Each filter in the layer is supposed to consistently generate image regions corresponding to the same visual concept when generating different images. The interpretable GAN learns to automatically discover meaningful visual concepts without any annotations of visual concepts. The interpretable GAN enables people to modify a specific visual concept on generated images by manipulating feature maps of the corresponding filters in the layer. Our method can be broadly applied to different types of GANs. Experiments have demonstrated the effectiveness of our method.

Publication
In Proceedings of the AAAI Conference on Artificial Intelligence (2022)
Jin Wang
Jin Wang
CS PhD Student @ HKU

My research interests include computer vision and machine learning.