Network Dissection: Quantifying Interpretability of Deep Visual Representations

19 April 2017

Antonio Torralba

Papers citing "Network Dissection: Quantifying Interpretability of Deep Visual Representations"

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One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Images Weina Jin Xiaoxiao Li Ghassan Hamarneh FAtt 45 16 0 11 Jul 2021
Using Causal Analysis for Conceptual Deep Learning Explanation Sumedha Singla Stephen Wallace Sofia Triantafillou Kayhan Batmanghelich CML 53 15 0 10 Jul 2021
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Subspace Clustering Based Analysis of Neural Networks Uday Singh Saini Pravallika Devineni Evangelos E. Papalexakis GNN 33 1 0 02 Jul 2021
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Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations Witold Oleszkiewicz Dominika Basaj Igor Sieradzki Michal Górszczak Barbara Rychalska K. Lewandowska Tomasz Trzciñski Bartosz Zieliñski SSL 75 3 0 21 Jun 2021
A Game-Theoretic Taxonomy of Visual Concepts in DNNs Xu Cheng Chuntung Chu Yi Zheng Jie Ren Quanshi Zhang 47 22 0 21 Jun 2021
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Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar Aleksander Madry FAtt 67 92 0 11 May 2021
Rationalization through Concepts Diego Antognini Boi Faltings FAtt 124 22 0 11 May 2021
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks Adrian Hoffmann Claudio Fanconi Rahul Rade Jonas Köhler 71 63 0 05 May 2021
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Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition Pooja Prajod Dominik Schiller Tobias Huber Elisabeth André CVBM 28 8 0 15 Apr 2021
An Interpretability Illusion for BERT Tolga Bolukbasi Adam Pearce Ann Yuan Andy Coenen Emily Reif Fernanda Viégas Martin Wattenberg MILM FAtt 103 82 0 14 Apr 2021
Automatic Correction of Internal Units in Generative Neural Networks A. Tousi Haedong Jeong Jiyeon Han Hwanil Choi Jaesik Choi GAN 57 11 0 13 Apr 2021