#ai #deeplearning #gan
GANs are of the main models in modern deep learning. This is the paper that started it all! While the task of image classification was making progress, the task of image generation was still cumbersome and prone to artifacts. The main idea behind GANs is to pit two competing networks against each other, thereby creating a generative model that only ever has implicit access to the data through a second, discriminative, model. The paper combines architecture, experiments, and theoretical analysis beautifully.
OUTLINE:
0:00 – Intro & Overview
3:50 – Motivation
8:40 – Minimax Loss Function
13:20 – Intuition Behind the Loss
19:30 – GAN Algorithm
22:05 – Theoretical Analysis
27:00 – Experiments
33:10 – Advantages & Disadvantages
35:00 – Conclusion
Paper:
Abstract:
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
Authors: Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
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