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Automatic Shortcut Removal For Self-Supervised Representation Learning.pdf

2002.08822.pdf
Preview of Automatic Shortcut Removal for Self-Supervised Representation Learning
🔗 Source: arxiv.org
📊 Size: 6.99 MB
👤 Author: Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
⬇️ Downloads: 151

Summary

Researchers propose a framework to mitigate the effect of "shortcut" features in self-supervised visual representation learning, where a neural network exploits low-level visual features to solve a pretext task instead of learning useful semantic representations. The method involves training a "lens" network to make small image changes that maximally reduce performance in the pretext task, and then using the modified images to train the feature extractor. This approach outperforms traditional methods and provides insights into how the choice of pretext task and dataset affects the features learned by self-supervision. The lens can also be used to visualize shortcuts and compare them across different pretext tasks and datasets.

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Description

Researchers propose a framework to mitigate the effect of "shortcut" features in self-supervised visual representation learning, where a neural network...

Technical Information

  • File Format: PDF
  • File Size: 6.99 MB
  • Pages: 20
  • Language: EN
  • Author: Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
  • Total Downloads: 151
  • Last Updated: 1 month ago

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This PDF document about Automatic Shortcut Removal for Self-Supervised Representation Learning provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Automatic Shortcut Removal for Self-Supervised Representation Learning.

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