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Feb, 2023
通过风险分解评估自监督学习
Evaluating Self-Supervised Learning via Risk Decomposition
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Yann Dubois, Tatsunori Hashimoto, Percy Liang
TL;DR
通过风险拆分的方法对自监督学习中的四个误差组件进行了有效估计,研究了30种不同的自监督学习设计选择,并给出了改进结果的可行方案。
Abstract
self-supervised learning
(SSL) pipelines differ in many
design choices
such as the architecture, augmentations, or pretraining data. Yet SSL is typically evaluated using a single metric: linear probing on ImageNe
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