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Dissection network

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Dissection network

  1. 1. Network Dissection Network Dissection: Quantifying Interpretability of Deep Visual Representation (CVPR2017 oral) Interpreting Deep Visual Representations via Network Dissection (TPAMI under review) Xiaolin Zhang
  2. 2. Introduction • What is a disentangled representation, and how can its factors be quantified and detected ? • Do interpretable hidden units reflect a special alignment of feature space? • What conditions (network architectures, data sources, and training conditions) affect the internal representations?
  3. 3. Quantitative measurement of interpretability • Manually identify 1197 visual concepts • Image-level annotation: scene, texture • Pixel-level annotation: object, part, material, color • Gather the response of the hidden variables to known concepts • Quantify alignment of hidden variable-concept pairs
  4. 4. Quantitative measurement of interpretability • Quantify alignment of hidden variable-concept pairs • Accuracy of unit k in detecting concept c (Data-set-wide intersection) • Unit k is a detector for concept c if IoUk,c > 0.04 • Top ranked label if chosen if a detector is for multiple concepts • The concept dictionary is limited(1197). • Unique detector: one detector to one concept
  5. 5. Human Evaluation of Interpretation • AlexNet trained on Places205
  6. 6. Measurement of Axis-Aligned Interpretability • Weather the interpretability is affected by rotation? • AlexNet, Places205, conv5(256)
  7. 7. Network Architectures with Supervised Learning • Interpretability: ResNet>VGG>GoogleNet>AlexNet • Places205>Imagenet
  8. 8. Captioning
  9. 9. Regularization
  10. 10. Training
  11. 11. Finetune
  12. 12. Layer Width
  13. 13. Discrimination vs. Interpretability

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