Of particular relevance to our work is Spectral Normalization (Miyato et al., 2018), which enforces Lipschitz continuity on D by normalizing its parameters with running estimates of their first singular values, inducing backwards dynamics t…We evaluate our models on ImageNet ILSVRC 2012 (Russakovsky et al., 2015) at 128×128, 256×256, and 512×512 resolutions, employing the settings from Table 1, row 8. The samples generated by our models are presented in Figure 4, with addition…Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger B. Grosse. On the quantitative analysis of decoder-based generative models. In ICLR, 2017.
Yasin Yazc, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, and Vijay Chand…
Han Zhang
scientist · 3 mentions across 1 reading
In this course
Han Zhang appears as a co-author on work involving large-scale generative image synthesis, likely contributing to methods for high-resolution image generation evaluated on ImageNet across multiple resolutions. The excerpts position this work within discussions of GAN regularization techniques like Spectral Normalization and decoder-based generative models, suggesting their contribution addresses the technical challenges of training stable, scalable generative systems. This work is cited as part of the course's broader investigation into how deep learning architectures learn to synthesize complex visual data—a key intersection of machine learning capability and aesthetic/representational questions in contemporary AI.
Mentioned in 1 reading
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Augustus Odena 2Takeru Miyato 2Chuan-Sheng Foo 1Dimitris Metaxas 1Georgios Piliouras 1Ian Goodfellow 1Karen Simonyan and Andrew Zisserman 1Kim-Hui Yap 1Roger B. Grosse 1Ruslan Salakhutdinov 1Stefan Winkler 1Vijay Chandrasekhar 1Yasin Yazc 1Yuhuai Wu 1Yuri Burda 1Harm de Vries 1Mehdi Mirza 1Tero Karras 1Xiaolong Wang 1Olga Russakovsky 1