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Improved Consistency Regularization for GANs
Spectral Normalization for GAN
Smoothness and Stability in GANs
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
Stabilizing Training of GANs Intuitive Introduction with Kevin Roth (ETH Zurich)
July 23rd 10 Towards a Better Understanding and Regularization of GAN Training Dynamics
Lipschitz Functions: Intro and Simple Explanation for Usefulness in Machine Learning
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
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Last Updated: October 19, 2026
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Summary
Self-Supervised learning describes a set of tasks that can learn useful representations without manual tuning. This paper explores ... All links are available in the blog post: itzikbs.com/ At Anyscale Learning For All (ALFA) research group (CSAIL MIT), we have designed and developed Lipizzaner framework, which ... This video explores a new technique for using the same Consistency Smoothness and Stability in GANs (ICLR 2020) Casey Chu, Kentaro Minami, Kenji Fukumizu arxiv.org/abs/2002.04185 ... Paper: arxiv.org/pdf/2006.10738.pdf Code: github.com/mit-han-lab/data-efficient-gans. July 23rd 10 Towards a Better Understanding and In a nutshell, saying a function is Paper: arxiv.org/abs/2111.06849 Code: github.com/EndlessSora/DeceiveD Project Page: ...
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