/SNGAN-AnimeFace

Unofficial PyTorch Implementation of Spectral Normalization for Generative Adversarial Networks (SNGAN) with specialization in Anime faces generation

Primary LanguagePython

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SNGAN on Anime Face Dataset

This repo provides an unofficial PyTorch Implementation of Spectral Normalization for Generative Adversarial Networks (SNGAN)1 with specialization in Anime faces2 generation.

Roadmap

  • original SNGAN

    • differentiable power iteration
    • $\gamma$-reparameterization
    • ResNet architecture
    • hinge loss 3

  • dataset-specific tuning

    • set $n_{dis} = 1$
    • use 1x1 conv instead of 3x3 conv for penultimate layer of G
    • replace avgpool2d down-sampling with stride-2 conv

  • TTUR4

  • mixed precision (-11% time)5

  • cuDNN benchmark

  • FID metrics + precomputed statistics

    • InceptionV3 pretrained on ImageNet is not suitable for anime data
    • data-specific feature extractor is needed

  • exponential moving average

  • visualizations 😄

Anime face generation

Generations from fixed noise

evolving over 50 training epochs

fixed-noise

Interpolation

interpolation

Truncation effect

from top to bottom: the variance varies from 0.3 to 3 cubically

truncation-effects

References

Footnotes

  1. Miyato, Takeru, et al. "Spectral Normalization for Generative Adversarial Networks." International Conference on Learning Representations. 2018. ↩

  2. Chao, Brian. ‘Anime Face Dataset: a collection of high-quality anime faces’. GitHub, https://github.com/bchao1/Anime-Face-Dataset. ↩

  3. Lim, Jae Hyun, and Jong Chul Ye. "Geometric gan." arXiv preprint arXiv:1705.02894 (2017). ↩

  4. Heusel, Martin, et al. "Gans trained by a two time-scale update rule converge to a local nash equilibrium." Advances in neural information processing systems 30 (2017). ↩

  5. "Automatic Mixed Precision For Deep Learning". NVIDIA Developer, https://developer.nvidia.com/automatic-mixed-precision. ↩