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Automatic Shortcut Removal for Self-Supervised Representation Learning
[article]
2020
arXiv
pre-print
In self-supervised visual representation learning, a feature extractor is trained on a "pretext task" for which labels can be generated cheaply, without human annotation. A central challenge in this approach is that the feature extractor quickly learns to exploit low-level visual features such as color aberrations or watermarks and then fails to learn useful semantic representations. Much work has gone into identifying such "shortcut" features and hand-designing schemes to reduce their effect.
arXiv:2002.08822v3
fatcat:xxzqyaocjrbmnlp5vydpjigbmm