Topic 0: Deep Generative Models

What does "Generative" mean in Deep Generative Models?

"Generative" describes a class of statistical models that contrasts with discriminative models.

Informally:

• Generative models can generate new data instances.

• Discriminative models discriminate between different kinds of data instances.

 

Example: A generative model could generate new photos of animals that look like real animals, while a discriminative model could tell a dog from a cat.

https://xzz201920.medium.com/generative-model-v-s-discriminative-model-e1586935d277

More formally, given a set of data instances X and a set of labels Y:

• Generative models capture the joint probability p(X, Y), or just p(X) if there are no labels.

• Discriminative models capture the conditional probability p(Y | X).

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What is the relation between Deep learning and Deep Generative Models?

Deep generative models are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions.

When trained successfully, we can use the DGM to estimate the likelihood of a given sample and to create new samples that are similar to samples from the unknown distribution. 

Three approaches to generative models:

Generative Adversarial Networks (GANs)

Variational Autoencoders (VAEs)

Auto-regressive models

 

 

 

 

GANs

2014

2014

ConditionalGANs

2015

LAPGANs

2015

DCGANs

2016

BiGANs

2016

SGAN

2016

InfoGANs

2016

EBGAN

Auxilary

Classifier

GANs

2017

2017

Progressive GANs

2018

BigGANs

2019

SAGANs

2019

NRGANs

2019

AutoGANs

2020

Your

Local GANs

2020

MSG-GANs

A generative adversarial network (GAN) is a class of Machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014

The main idea behind it is "Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent's gain is another agent's loss)".

GANs and its History:

Ian Goodfellow

Variational Autoencoders(VAEs):

The main idea is "to learn a low-dimensional latent representation of the training data called latent variables (variables which are not directly observed but are rather inferred through a mathematical model) which we assume to have generated our actual training data".

 

https://medium.com/@realityenginesai/understanding-variational-autoencoders-and-their-applications-81a4f99efc0d

Auto-regressive Models/Networks:

DARNs (Deep AutoRegressive Networks) are generative sequential models, and are therefore often compared to other generative networks like GANs or VAEs; however, they are also sequence models and show promise in traditional sequence challenges like language processing and audio generation.

First, applications. Research in autoregressive networks tends toward two large subject areas: image generation and sequence modelling with a particular emphasis on audio generation and text to speech. Like PixelCNN is one of the best known autoregressive networks.

And more over autoregressive models such as PixelRNN instead train a network that models the conditional distribution of every individual pixel given previous pixels (to the left and to the top)

 

Intutive Picture of Deep Generative Models

https://ml.berkeley.edu/blog/posts/AR_intro/

• Text-to-Image Translation  

• Image to Image Translation                                                            

• Generate Photographs of Human Faces

• 3D Object Generation

• Semantic-Image-to-Photo Translation

• Photo Inpainting

• Video Prediction

• Face Aging

• Super Resolution

• Clothing Translation

 

 

 

 

 

 

 

Applications of Deep Generative Models: