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Challenging Common Assumptions In The Unsupervised Learning Of Disentangled Representations.pdf

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Preview of Challenging common assumptions in the unsupervised learning of disentangled representations
🔗 Source: proceedings.mlr.press
📊 Size: 9.36 MB
👤 Author: Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Ol
⬇️ Downloads: 197

Summary

The unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. A large-scale experimental study of over 12,000 models on seven data sets found that while different methods can enforce desired properties, well-disentangled models cannot be identified without supervision. Increased disentanglement does not lead to decreased sample complexity of learning for downstream tasks, suggesting that future work should focus on the role of inductive biases and implicit supervision, and investigate concrete benefits of disentanglement.

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The unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data.

Technical Information

  • File Format: PDF
  • File Size: 9.36 MB
  • Pages: 37
  • Language: EN
  • Author: Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Ol
  • Total Downloads: 197
  • Last Updated: 8 hours ago

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This PDF document about Challenging common assumptions in the unsupervised learning of disentangled representations provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Challenging common assumptions in the unsupervised learning of disentangled representations.

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