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1704.05796
Cited By
Network Dissection: Quantifying Interpretability of Deep Visual Representations
19 April 2017
David Bau
Bolei Zhou
A. Khosla
A. Oliva
Antonio Torralba
MILM
FAtt
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Papers citing
"Network Dissection: Quantifying Interpretability of Deep Visual Representations"
50 / 842 papers shown
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty
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One Explanation is Not Enough: Structured Attention Graphs for Image Classification
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Li Fuxin
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294
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Role Taxonomy of Units in Deep Neural Networks
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Hao Zhang
Xiuyuan Hu
103
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246
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201
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79
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16 Oct 2020
Interpreting Deep Learning Model Using Rule-based Method
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98
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Xiang Cheng
101
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Unsupervised Point Cloud Pre-Training via Occlusion Completion
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Hanchen Wang
Qi Liu
Xiangyu Yue
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303
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Liang Gou
Lincan Zou
Nanxiang Li
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258
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Stella X. Yu
152
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Xiang Ao
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154
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21 Sep 2020
Contextual Semantic Interpretability
Asian Conference on Computer Vision (ACCV), 2020
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Ruth C. Fong
Sylvain Lobry
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267
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Felipe del-Rio
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151
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365
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07 Sep 2020
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319
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Haekyu Park
Zijie J. Wang
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127
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What is being transferred in transfer learning?
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Behnam Neyshabur
Hanie Sedghi
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370
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272
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170
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Ruigang Fu
Qingyong Hu
Xiaohu Dong
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FAtt
372
334
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159
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159
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Peixian Chen
Qiong Wu
Xiaopeng Hong
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Rongrong Ji
OOD
110
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27 Jul 2020
Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction
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193
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170
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Volumetric Transformer Networks
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Sabine Süsstrunk
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199
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164
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288
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86
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Locality Guided Neural Networks for Explainable Artificial Intelligence
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115
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Scientific Discovery by Generating Counterfactuals using Image Translation
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Arunachalam Narayanaswamy
Subhashini Venugopalan
D. Webster
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173
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Concept Bottleneck Models
International Conference on Machine Learning (ICML), 2020
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Thao Nguyen
Y. S. Tang
Stephen Mussmann
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443
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131
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155
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