Implement GANs
- Simple-GAN
- DC-GAN => https://arxiv.org/pdf/1511.06434.pdf
- Wasserstein- GAN => https://arxiv.org/pdf/1806.06621.pdf
- SR-GAN => https://arxiv.org/pdf/1609.04802.pdf
- Cycle-GAN => https://arxiv.org/pdf/1703.10593.pdf
- Pix2Pix => https://arxiv.org/pdf/1611.07004.pdf
- Wasserstein GAN-GP => https://arxiv.org/abs/1704.00028
- STAR-GAN => https://arxiv.org/pdf/1711.09020.pdf
- LS-GAN => https://arxiv.org/abs/1611.04076
- Softmax-GAN => https://arxiv.org/pdf/1704.06191.pdf
- Conditional-GAN => https://arxiv.org/abs/1411.1784
- DRAGAN => https://arxiv.org/abs/1705.07215
- StyleGAN2 => https://arxiv.org/abs/1912.04958
- U-GAT-IT => https://arxiv.org/abs/1907.10830
- TransGAN => https://arxiv.org/abs/2102.07074
- Swin Transformer => https://arxiv.org/abs/2103.14030
- StyleSwin => https://arxiv.org/abs/2112.10762
- CUT => https://arxiv.org/pdf/2007.15651.pdf
- Vid2Vid => https://arxiv.org/abs/1808.06601
- MUNIT => https://arxiv.org/abs/1804.04732
- Attention CycleGAN => https://arxiv.org/abs/1806.02311
- DRIT ++ => https://arxiv.org/abs/1905.01270
- StarGANv2 => https://arxiv.org/abs/1912.01865
- SAGAN => https://arxiv.org/abs/1805.08318
- VQ-GAN => https://arxiv.org/abs/2012.09841
- DDPM => https://arxiv.org/abs/2006.11239
- DDIM => https://arxiv.org/abs/2010.02502