Gan summary

Jul 23, 20180 likes328 views

This document provides an outline and overview of generative adversarial networks (GANs). It introduces GAN theory and common issues with GANs like mode collapse. It then summarizes different types of GANs including WGAN, EBGAN, LSGAN, and DCGAN. The document also mentions using GANs for feature extraction and conditional generation.

GAN summary
Liangqun Lu
2018-07-13
*Most slides are from Hung-yi Lee ‘Generative Adversarial Network
(GAN), 2018’, videos and slides

Outline
● GAN introduction
○ GAN theory
○ Issues and Solutions
● Examples from GAN Zoo
○ WGAN and WGAN-GP
○ EBGAN
○ LSGAN
○ DCGAN
● Feature extraction-based GANs
○ Conditional generation
○ Unsupervised conditional generation

● Discriminator learns to assign high scores to real objects and low scores to
generated objects.
● Generator learns to “fool” the discriminator

GAN issues and solutions
● JS convergence
● Wasserstein distance
● Using an autoencoder as discriminator D
● Mode Collapse and Dropping
● GAN ensemble
● Inception score

DCGAN
Source: /shyamkkhadka/unsupervised-learning-represenation-with-dcgan

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Gan summary

1. GAN summary Liangqun Lu 2018-07-13 *Most slides are from Hung-yi Lee ‘Generative Adversarial Network (GAN), 2018’, videos and slides

2. Outline ● GAN introduction ○ GAN theory ○ Issues and Solutions ● Examples from GAN Zoo ○ WGAN and WGAN-GP ○ EBGAN ○ LSGAN ○ DCGAN ● Feature extraction-based GANs ○ Conditional generation ○ Unsupervised conditional generation

3. ● Discriminator learns to assign high scores to real objects and low scores to generated objects. ● Generator learns to “fool” the discriminator

14. GAN issues and solutions ● JS convergence ● Wasserstein distance ● Using an autoencoder as discriminator D ● Mode Collapse and Dropping ● GAN ensemble ● Inception score

32. DCGAN Source: /shyamkkhadka/unsupervised-learning-represenation-with-dcgan

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Gan summary

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