1. Unsupervised Learning

Supervised vs Unsupervised Learning:

2. Generative Models

概述：

Generative Models的作用：

Generative Models的分类：

3. PixelRNN and PixelCNN

基本原理：

PixelRNN：

PixelCNN：

Training is faster than PixelRNN (can parallelize convolutions since context region values known from training images)
Generation must still proceed sequentially=> still slow

Generation Samples：

PixelRNN and PixelCNN

4. Variational Autoencoders (VAE)

4.1 与PixelRNN/PixelCNN的比较：

4.2 Some background first: Autoencoders：

Tips:

如果将其用于特征提取，则在训练之后，将decoder部分丢弃！

Autoencoders can reconstruct data, and can learn features to initialize a supervised model！

4.3 Variational Autoencoders

利用高斯分布随机生成特征Z：

Variational Autoencoders: Intractability

$$pθ(z)$$ 跟据高斯分布随机获得， $$pθ(x|z)$$ 根据decoder net获得，而为每个z计算 $$pθ(x|z)$$ 并最终积分得到 $$pθ(x)$$ 是不可能的！

解决办法：

如何进行优化：

4.4 Generating Data

4.5 性能分析：

5. Generative Adversarial Networks (GAN)

回顾：

5.1 Training GANs: Two-player game

Generator network: try to fool the discriminator by generating real-looking images
Discriminator network: try to distinguish between real and fake images

网络优化：

优化存在的问题：

解决办法：

GAN training algorithm：

5.2 Generative Adversarial Nets

Generated samples:

Generative Adversarial Nets: Convolutional Architectures

Generator is an upsampling network with fractionally-strided convolutions
Discriminator is a convolutional network

Generator网络结构：

Samples from the model look amazing!

Generative Adversarial Nets: Interpretable Vector Math

GANs的优缺点：

6. 回顾：