Methods › General › Interpretability › Network Dissection
Network Dissection
Introduced by Bolei Zhou et al. in Interpreting Deep Visual Representations via Network Dissection
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Network Dissection is an interpretability method for CNNs that evaluates the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels across a range of objects, parts, scenes, textures, materials, and colors.
The measurement of interpretability proceeds in three steps:
- Identify a broad set of human-labeled visual concepts.
- Gather the response of the hidden variables to known concepts.
- Quantify alignment of hidden variable−concept pairs.
Papers archive 2025-07-28
11 shown of 11, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Labeling Neural Representations with Inverse Recognition 22 Nov 2023 · 2 repositories · arXiv:2311.13594Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
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DISCOVER: Making Vision Networks Interpretable via Competition and Dissection 21 Sep 2023 · 1 repository
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On the Impact of Knowledge Distillation for Model Interpretability 25 May 2023 · 0 repositories · arXiv:2305.15734
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Detection Accuracy for Evaluating Compositional Explanations of Units 16 Sep 2021 · 1 repository · arXiv:2109.07804
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Interpreting Face Inference Models using Hierarchical Network Dissection 23 Aug 2021 · 1 repository · arXiv:2108.10360
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Gated Convolutional Networks with Hybrid Connectivity for Image Classification 26 Aug 2019 · 1 repository · arXiv:1908.09699Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
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Interpreting Adversarial Examples by Activation Promotion and Suppression 3 Apr 2019 · 0 repositories · arXiv:1904.02057
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On the Units of GANs (Extended Abstract) 29 Jan 2019 · 0 repositories · arXiv:1901.09887
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GAN Dissection: Visualizing and Understanding Generative Adversarial Networks 26 Nov 2018 · 8 repositories · arXiv:1811.10597Syntology ran 4 of 34 samples · 30 unverified
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How convolutional neural network see the world - A survey of convolutional neural network visualization methods 30 Apr 2018 · 1 repository · arXiv:1804.11191
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Interpreting Deep Visual Representations via Network Dissection 15 Nov 2017 · 3 repositories · arXiv:1711.05611
Tasks archive 2025-07-28
16 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections