Day #155 - Deep Generative Models

"Fake it Until It can't figureout it is Fake"

• Train Neural Networks from Training Examples for Sample Generations from training data 
• Generative Models for Outlier Detection
• Neural Machine Translations
• Generative networks support Reinforcement Learning for Robotics

Kinds of Generative Models

• Autoregressive models - Deep NADE, PixelRNN, WaveNet, Video Pixel Network
• Latent Variable Models - Variational Auto Encoders, General Adverserial Networks

Latent Variable Models

• Latent variables that represent variations in data
• They move the data (Smile appearance, Illumination)
• Find variables that give variations in data

Variational Autoencoder

• Latent Variable Models
• Model discovered independent variables causing variations in data
• Some distribution over data, maximize the likelihood
• Posterior of Z given X
• Includes Encoder + Decoder + Regularization of Posterior to look like prior
• GAN - Generator (Prepares data to fool discriminator), Discriminator - (Difference between true data and fake data done by generator)
• CGAN, Least Squares GAN
• Cycle Consistent Adverserial Networks

What makes GAN Special ?

• Image manifold is complicated non-linearity
• We do random sampling
• Maxlikelihood (certain density for every sample it provides)

Happy Mastering DL!!!