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A Cookbook of Self-Supervised Learning
- Publication Year :
- 2023
-
Abstract
- Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art with a high barrier to entry. While many components are familiar, successfully training a SSL method involves a dizzying set of choices from the pretext tasks to training hyper-parameters. Our goal is to lower the barrier to entry into SSL research by laying the foundations and latest SSL recipes in the style of a cookbook. We hope to empower the curious researcher to navigate the terrain of methods, understand the role of the various knobs, and gain the know-how required to explore how delicious SSL can be.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2304.12210
- Document Type :
- Working Paper