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To interpret Vision Transformers, post-hoc explanations assign salience scores to input pixels, providing human-understandable heatmaps.
A new measure of rank correlation
Maurice G Kendall · 1938
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2016
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Visualizing deep convolutional neural networks using natural pre-images
Aravindh Mahendran and Andrea Vedaldi · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
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Human-in-the-loop interpretability prior
Isaac Lage, Andrew Ross, Samuel J Gershman, Been Kim, and Finale Doshi-Velez · 2018
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Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Ross and Finale Doshi-Velez · 2018
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Interpreting deep visual representations via network dissection
Bolei Zhou, David Bau, Aude Oliva, and Antonio Torralba · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Understanding deep networks via extremal perturbations and smooth masks
Ruth Fong, Mandela Patrick, and Andrea Vedaldi · 2019
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Attention is not explanation
Attention is not only a weight: Analyzing transformers with vector norms
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui · 2020
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Relative attributing propagation: Interpreting the comparative contributions of individual units in deep neural networks
Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf, and Seong-Whan Lee · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Visualization of supervised and self-supervised neural networks via attribution guided factorization
Shir Gur, Ameen Ali, and Lior Wolf · 2021
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The out-of-distribution problem in explainability and search methods for feature importance explanations
Peter Hase, Harry Xie, and Mohit Bansal · 2021
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Sarthak Jain and Byron C Wallace · 2019
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Is attention interpretable?
Sofia Serrano and Noah A Smith · 2019
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Full-gradient representation for neural network visualization
Suraj Srinivas and François Fleuret · 2019
Cited alongside, same era.
Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov · 2019
Cited alongside, same era.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
Cited alongside, same era.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
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A diagnostic study of explainability techniques for text classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein · 2020
Cited alongside, same era.
IA-RED 2 \text{IA-RED}^{2} : Interpretability-aware redundancy reduction for vision transformers
Bowen Pan, Rameswar Panda, Yifan Jiang, Zhangyang Wang, Rogerio Feris, and Aude Oliva · 2021
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Do input gradients highlight discriminative features?
Harshay Shah, Prateek Jain, and Praneeth Netrapalli · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Openxai: Towards a transparent evaluation of model explanations
Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju · 2022
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Xai for transformers: Better explanations through conservative propagation
Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, and Lior Wolf · 2022
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What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods
Julien Colin, Thomas Fel, Rémi Cadène, and Thomas Serre · 2022
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Rethinking attention-model explainability through faithfulness violation test
Yibing Liu, Haoliang Li, Yangyang Guo, Chenqi Kong, Jing Li, and Shiqi Wang · 2022
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Attcat: Explaining transformers via attentive class activation tokens
Yao Qiang, Deng Pan, Chengyin Li, Xin Li, Rhongho Jang, and Dongxiao Zhu · 2022
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A consistent and efficient evaluation strategy for attribution methods
Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, and Enkelejda Kasneci · 2022
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A unified study of machine learning explanation evaluation metrics
Yipei Wang and Xiaoqian Wang · 2022
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Shap-cam: Visual explanations for convolutional neural networks based on shapley value
Quan Zheng, Ziwei Wang, Jie Zhou, and Jiwen Lu · 2022
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Token transformation matters: Towards faithful post-hoc explanation for vision transformer
Junyi Wu, Bin Duan, Weitai Kang, Hao Tang, and Yan Yan · 2024
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