feat(models): Add GAN to Modelhub (#5)

models/megengine_vision_gan.md

+---
+template: hub1
+title: Generative Adversarial Networks
+summary:
+    en_US: GAN pre-trained on Cifar10
+    zh_CN: 生产对抗网络（Cifar10 预训练权重）
+author: MegEngine Team
+tags: [vision, gan]
+github-link: https://github.com/MegEngine/Models/tree/master/official/vision/gan
+---
+
+<!-- section: zh_CN -->
+
+#### 训练参数
+| 分辨率 | 批大小 | 学习率 | β<sub>1</sub> | β<sub>2</sub> | 衰减法则 | n<sub>dis</sub> | n<sub>iter</sub> |
+|:----------:|:----------:|:-------------:|:-------------:|:-------------:|:------------:|:---------------:|------------------|
+| 32 x 32 | 64 | 2e-4 | 0.0 | 0.9 | Linear | 5 | 100K |
+
+
+#### 评测指标
+| Metric | Method |
+|:--------------------------------:|:---------------------------------------:|
+| [Inception Score (IS)](https://arxiv.org/abs/1606.03498) | 分成10份共计 50K 样本 |
+| [Fréchet Inception Distance (FID)](https://arxiv.org/abs/1706.08500) | 50K 真实/生成样本 |
+| [Kernel Inception Distance (KID)](https://arxiv.org/abs/1801.01401) | 50K 真实/生成样本， 分成10份取平均值|
+
+
+#### Cifar10 结果
+| 模型 | FID Score | IS Score | KID Score |
+| :-: | :-: | :-: | :-: |
+| DCGAN  | 27.2 | 7.0 | 0.0242 |
+| WGAN-WC  | 30.5  | 6.7 | 0.0249 |
+
+
+### 参考文献
+
+ - [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), Alec Radford, Luke Metz, and Soumith Chintala.
+ - [Wasserstein GAN](https://arxiv.org/abs/1701.07875), Martin Arjovsky, Soumith Chintala, and Léon Bottou.
+
+<!-- section: en_US -->
+
+#### Training Parameters
+| Resolution | Batch Size | Learning Rate | β<sub>1</sub> | β<sub>2</sub> | Decay Policy | n<sub>dis</sub> | n<sub>iter</sub> |
+|:----------:|:----------:|:-------------:|:-------------:|:-------------:|:------------:|:---------------:|------------------|
+| 32 x 32 | 64 | 2e-4 | 0.0 | 0.9 | Linear | 5 | 100K |
+
+
+#### Metrics
+| Metric | Method |
+|:--------------------------------:|:---------------------------------------:|
+| [Inception Score (IS)](https://arxiv.org/abs/1606.03498) | 50K samples at 10 splits|
+| [Fréchet Inception Distance (FID)](https://arxiv.org/abs/1706.08500) | 50K real/generated samples |
+| [Kernel Inception Distance (KID)](https://arxiv.org/abs/1801.01401) | 50K real/generated samples, averaged over 10 splits.|
+
+
+#### Cifar10 Results
+| Method | FID Score | IS Score | KID Score |
+| :-: | :-: | :-: | :-: |
+| DCGAN  | 27.2 | 7.0 | 0.0242 |
+| WGAN-WC  | 30.5  | 6.7 | 0.0249 |
+
+### References
+
+ - [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), Alec Radford, Luke Metz, and Soumith Chintala.
+ - [Wasserstein GAN](https://arxiv.org/abs/1701.07875), Martin Arjovsky, Soumith Chintala, and Léon Bottou.