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Computer Science > Machine Learning

arXiv:2304.12210 (cs)
[Submitted on 24 Apr 2023]

Title:A Cookbook of Self-Supervised Learning

Authors:Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos,
Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes,
Gregoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon
Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar,
Hamed Pirsiavash, Yann LeCun, Micah Goldblum
Download a PDF of the paper titled A Cookbook of Self-Supervised
Learning, by Randall Balestriero and 17 other authors
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    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.

Subjects: Machine Learning (cs.LG); Computer Vision and Pattern
          Recognition (cs.CV)
Cite as:  arXiv:2304.12210 [cs.LG]
          (or arXiv:2304.12210v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2304.12210
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Mark Ibrahim [view email]
[v1] Mon, 24 Apr 2023 15:49:53 UTC (2,305 KB)
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