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🔗 Source: hci.iwr.uni-heidelberg.de
📊 Size: 13.07 MB
📄 Pages: 19 pages
⬇️ Downloads: 254

Summary

Neural networks learn powerful representations, but their hidden layers lack interpretability due to distributed coding, making it hard to attribute meaning to individual neurons. The proposed invertible interpretation network translates hidden representations into semantic concepts, allowing for disentanglement into separate, meaningful concepts without modifying the original network. This approach enables interpretation of existing classification and image generation networks, as well as semantically guided image manipulation, and can be applied to arbitrary existing models without affecting their performance.

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Description

Neural networks learn powerful representations, but their hidden layers lack interpretability due to distributed coding, making it hard to attribute meaning to...

Technical Information

  • File Format: PDF
  • File Size: 13.07 MB
  • Pages: 19
  • Language: EN
  • Total Downloads: 254
  • Last Updated: 2 hours ago

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