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(2015) Unsupervised representation learning with deep convolutional generative adversarial networks

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Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.

该篇论文提出Deep Convolutional GANs结构 (Figure 1)，使用一些方法来提高train稳定性，并通过实验验证

  • D的性能
  • 可视化D的特征图
  • G的Walking in the Latent Space、遗忘性和Vector Arithmetic.


1. 提高训练稳定性的方法

  • Stride Conv 替代 Pooling
  • Eliminate FC层（相对于GAN中的FC而言）
  • BN层，除G的输出层和D的输入层外，否则导致不稳定
  • G使用ReLU，输出层使用Tanh。D使用LeakyReLU**

2. 验证D的性能

使用D作为Feature Extractor来classify CIFAR-10和SVHN。

3. D特征图可视化

不同特征图activate on 不同objects (Figure 5) 。



4. Walking in the Latent Space



5. G的遗忘性

G能学到不同object的表达,在second highest Conv层(倒数第二层)的特征上，利用logistic regression 预测activate窗户的filters，比较drop out窗户相关filters与否的生成结果。在drop out窗户filter的生成结果中，一些图片去掉了窗户,一些图片生成相似的其他object，如门、镜子 (Figure 6)。



6. Vector Arithmetic of Z

类似于Word2vec of Mikolov (Figure 7)。
Single sample per concept were unstable. Average $Z$ of 3 sample show consistence andstable.



7. Train DCGAN on MNIST

  • We found that removing the scale and bias parameters from batchnorm produced better results for both models.
  • Noise introduced by batchnorm helps the generative models to better explore and generate from the underlying data distribution.

8. Code

Code comes from github AaronYALai/Generative_Adversarial_Networks_PyTorch.

  • Code of GAN



  • Code of DCGAN