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Image GPT For Unsupervised Learning Without Domain Knowledge And Image Generation Is Awesome! (Representation Learning Of Images Summer 2020 Feature 1)

Image GPT For Unsupervised Learning Without Domain Knowledge And Image Generation Is Awesome! (Representation Learning Of Images Summer 2020 Feature 1)

Image Recognition

3 main points
✔️ Unknown Domain Knowledge Unsupervised Representation Learning Successful, Valuable Proof-of-Concept (PoC), Ignoring the Computational Complexity 
✔️ Amazing image generation capability even as a generation model

✔️ SOTA performance with image classification using acquired representations

Generative Pretraining from Pixels
written by 
(Submitted on 17 Jun 2020)

Comments: Accepted at ICML2020
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Paper  Official Code 
COMM Code 

In this three-part series, we will introduce various methods of unsupervised learning in a special feature entitled "Learning to Represent Images in Summer 2020". Perhaps because this field has reached a certain level of performance, many different methods have been proposed, and the game is heating up. I would like to present a selection of these methods.

Introduction - The Significance of Unsupervised + Expressive Learning

Since this is an introduction to papers that deal primarily with images throughout this series, we'll use a dataset like CIFAR-10 to illustrate this. When we do "unsupervised learning" on a dataset like this, we can think of a case where we would be clustering without using labels at all.

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