Projects per year
In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains. The controllable synthesis ability of GAN generators suggests that they maintain informative, disentangled, and explainable image representations, but leveraging and transferring their representations to downstream tasks is largely unexplored. In this paper, we propose to distill knowledge from GAN generators by squeezing and spanning their representations. We squeeze the generator features into representations that are invariant to semantic-preserving transformations through a network before they are distilled into the student network. We span the distilled representation of the synthetic domain to the real domain by also using real training data to remedy the mode collapse of GANs and boost the student network performance in a real domain. Experiments justify the efficacy of our method and reveal its great significance in self-supervised representation learning. Code will be made public.
|Title of host publication||Neural Information Processing Systems 2022(NeurIPS)|
|Number of pages||14|
|Publication status||Accepted/In press - 14 Sep 2022|
|Event||The 36th Conference on Neural Information Processing Systems, 2022 - New Orleans, United States|
Duration: 28 Nov 2022 → 9 Dec 2022
Conference number: 36
|Conference||The 36th Conference on Neural Information Processing Systems, 2022|
|Abbreviated title||NeurIPS 2022|
|Period||28/11/22 → 9/12/22|
FingerprintDive into the research topics of 'Distilling Representations from GAN Generator via Squeeze and Span'. Together they form a unique fingerprint.
- 1 Active
Visual AI: An Open World Interpretable Visual Transformer
1/12/20 → 30/11/25