Disentangling Regional Primitives for Image Generation

Zhengting Chen, Lei Cheng, Lianghui Ding, Quanshi Zhang

Keywords: Explainable AI, Generative Adversarial Networks, Interactions

TL;DR: This paper presents a method to explain the internal representation structure of a neural network for image generation.

Abstract: This paper presents a method to explain the internal representation structure of a neural network for image generation. Specifically, our method disentangles primitive feature components from the intermediate-layer feature of the neural network, which ensures that each feature component is exclusively used to generate a specific set of image regions. In this way, the generation of the entire image can be considered as the superposition of different pre-encoded primitive regional patterns, each being generated by a feature component. We find that the feature component can be represented as an OR relationship between the demands for generating different image regions, which is encoded by the neural network. Therefore, we extend the Harsanyi interaction to represent such an OR interaction to disentangle the feature component. Experiments show a clear correspondence between each feature component and the generation of specific image regions.

Primary Area: interpretability and explainable AI

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Submission Number: 2484