Why I Stopped Using GAN — ECCV 2020

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Why I Stopped Using GAN — ECCV 2020

GAN — vs — Normalizing FlowThe benefits of Normalizing Flow. In this article, we show how we outp... GAN — vs — Normalizing FlowThe benefits of Normalizing Flow. In this article, we show how we outperformed GAN with Normalizing Flow. We do that based on the application super-resolution. There we describe SRFlow, a super-resolution method that outperforms state-of-the-art GAN approaches. We explain it in detail in our ECCV 2020 paper. Intuition for Conditional Normalizing Flow. We train a Normaliz

rawwell "Conditional GANs need careful hyperparameter tuning. As seen in many works that use conditional GAN, the loss comprises of a weighted sum of multiple losses. Zhu et al. developed CycleGAN and carefully tuned the weights of eight different losses. In addition, they had to balance the generator and "

2020/09/08 リンク