GANs: Building Blocks

Get familiar with the basic components required for GAN construction.

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The discriminator
The generator
Real and fake data
Random noise
Discriminator and generator loss

A GAN is informally described as an iterative game played between a detective and a counterfeiter; while the detective’s goal is to minimize its loss by learning to identify real data as real and fake data as fake, the counterfeiter’s goal is to minimize its loss by learning to fool the detective by transforming random noise into fake data. This informal definition can be described using the following figureSource: Generative Adversarial Networks – A Deep Learning Architecture (https://hackernoon.com/generativeadversarial-networks-a-deep-learning-architecture-4253b6d12347):

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Getting Started

Deep Learning Basics and Environment Test

Introduction to Generative Models

Implementing Our First GAN

Evaluating Our First GAN

Improving Our First GAN

Synthesizing and Manipulating Images with GANs

Progressive Growing of GANs

Generation of Discrete Sequences Using GANs

Text-to-Image Synthesis with GANs

Speech Enhancement with GANs

TequilaGAN—Identifying GAN Samples

What’s Next in GANs

Conclusion

Appendix

GANs: Building Blocks