Interpreting Generative Adversarial Networks for Interactive Image Generation
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Abstract (arXiv)
Significant progress has been made by the advances in Generative Adversarial Networks (GANs) for image generation. However, there lacks enough understanding of how a realistic image is generated by the deep representations of GANs from a random vector. This chapter gives a summary of recent works on interpreting deep generative models. The methods are categorized into the supervised, the unsupervised, and the embedding-guided approaches. We will see how the human-understandable concepts that emerge in the learned representation can be identified and used for interactive image generation and editing.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 22 references that are in the map. First posted on arXiv 10 Aug 2021.
Builds on
20 of 22 references.
References · 20
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8 more
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Citing papers per year: 2021: 1; 2022: 2.
3 papers.