Unsupervised representation learning with deep convolutional generative adversarial networks.md

论文：《Unsupervised representation learning with deep convolutional generative adversarial networks》

Contribution:

• We propose and evaluate a set of constraints on the architectural topology of Convolutional GANs that make them stable to train in most settings. We name this class of architectures Deep Convolutional GANs (DCGAN)
• We use trained discriminators for image classification tasks, showing competitive performance with other unsupervised algorithms
• We visualize the filters learnt by GANs and empirically show that specific filters have learned to draw specific objects.
• We show that the generators have interesting vector arithmetic properties allowing for easy manipulation of many semantic qualities of generated samples

Guidelines：

fractional-strided convolution: transposed convolution, deconvolution

Details:

• input scale to the range of tanh activation functio [-1, 1]
• SGD, batch_size = 128
• weights are initialized from N(0, 0.02^2)
• In the LeakyReLU, the slope is 0.2.
• optimizer: Adam, lr=0.0002, momentum=0.5

Evaluation

验证无监督表示学习的最好方法，是将该模型输出的特征，作为一个分类模型的输入，在有监督数据集上，做分类检测查看效果。

Conclusion and Future Work

There are still some forms of model instability remaining - we noticed as models are trained longer they sometimes collapse a subset of filters to a single oscillating mode.