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Transformers have had a significant impact on natural language processing and have recently demonstrated their potential in computer vision.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Imagenet auto-annotation with segmentation propagation
Matthieu Guillaumin, Daniel Küttel, and Vittorio Ferrari · 2014
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Deep inside convolutional networks: visualizing image classification models and saliency maps
K Simonyan, A Vedaldi, and A Zisserman · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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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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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, et al · 2015
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Grad-cam: Why did you say that?
Ramprasaath R Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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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 · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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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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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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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
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Sarthak Jain and Byron C Wallace · 2019
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Understanding neural networks via feature visualization: A survey
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2019
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Generalized intersection over union: A metric and a loss for bounding box regression
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Explainable AI: interpreting, explaining and visualizing deep learning , volume 11700
Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller · 2019
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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.
On the (in) fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar · 2019
Cited alongside, same era.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
Cited alongside, same era.
Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera · 2020
Cited alongside, same era.
Evaluating and aggregating feature-based model explanations
Umang Bhatt, Adrian Weller, and José MF Moura · 2020
Cited alongside, same era.
Interpretdl: Explaining deep models in paddlepaddle
Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen, and Dejing Dou · 2022
Later among the works it cites.
Not all patches are what you need: Expediting vision transformers via token reorganizations
Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, and Pengtao Xie · 2022
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Attention-based interpretability with concept transformers
Mattia Rigotti, Christoph Miksovic, Ioana Giurgiu, Thomas Gschwind, and Paolo Scotton · 2022
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Patch slimming for efficient vision transformers
Yehui Tang, Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, and Dacheng Tao · 2022
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Vit-cx: Causal explanation of vision transformers
Weiyan Xie, Xiao-Hui Li, Caleb Chen Cao, and Nevin L Zhang · 2022
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
On the relationship between self-attention and convolutional layers
Jean-Baptiste Cordonnier, Andreas Loukas, and Martin Jaggi · 2020
Cited alongside, same era.
How do decisions emerge across layers in neural models? interpretation with differentiable masking
Nicola De Cao, Michael Schlichtkrull, Wilker Aziz, and Ivan Titov · 2020
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, et al · 2020
Cited alongside, same era.
On quantitative aspects of model interpretability
An-phi Nguyen and María Rodríguez Martínez · 2020
Cited alongside, same era.
Grad-sam: Explaining transformers via gradient self-attention maps
Oren Barkan, Edan Hauon, Avi Caciularu, Ori Katz, Itzik Malkiel, Omri Armstrong, and Noam Koenigstein · 2021
Cited alongside, same era.
Explainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelligence
Sajid Ali, Tamer Abuhmed, Shaker El-Sappagh, Khan Muhammad, Jose M Alonso-Moral, Roberto Confalonieri, Riccardo Guidotti, Javier Del Ser, Natalia Díaz-Rodríguez, and Francisco Herrera · 2023
Closest in time.
Holistically explainable vision transformers
Moritz Böhle, Mario Fritz, and Bernt Schiele · 2023
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Beyond intuition: Rethinking token attributions inside transformers
Jiamin Chen, Xuhong Li, Lei Yu, Dejing Dou, and Haoyi Xiong · 2023
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Learning to estimate shapley values with vision transformers
Ian Connick Covert, Chanwoo Kim, and Su-In Lee · 2023
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Atman: Understanding transformer predictions through memory efficient attention manipulation
Mayukh Deb, Björn Deiseroth, Samuel Weinbach, Patrick Schramowski, and Kristian Kersting · 2023
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An analysis of explainability methods for convolutional neural networks
Lynn Vonder Haar, Timothy Elvira, and Omar Ochoa · 2023
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Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M-C Höhne · 2023
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Generating images with multimodal language models
Jing Yu Koh, Daniel Fried, and Ruslan Salakhutdinov · 2023
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Towards evaluating explanations of vision transformers for medical imaging
Piotr Komorowski, Hubert Baniecki, and Przemyslaw Biecek · 2023
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Llm itself can read and generate cxr images
Suhyeon Lee, Won Jun Kim, and Jong Chul Ye · 2023
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A survey of visual transformers
Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao Shi, Jianping Fan, and Zhiqiang He · 2023
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Visualizing and understanding patch interactions in vision transformer
Jie Ma, Yalong Bai, Bineng Zhong, Wei Zhang, Ting Yao, and Tao Mei · 2023
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Vision diffmask: Faithful interpretation of vision transformers with differentiable patch masking
Angelos Nalmpantis, Apostolos Panagiotopoulos, John Gkountouras, Konstantinos Papakostas, and Wilker Aziz · 2023
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R-cut: Enhancing explainability in vision transformers with relationship weighted out and cut
Yingjie Niu, Ming Ding, Maoning Ge, Robin Karlsson, Yuxiao Zhang, and Kazuya Takeda · 2023
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Attentionviz: A global view of transformer attention
Catherine Yeh, Yida Chen, Aoyu Wu, Cynthia Chen, Fernanda Viégas, and Martin Wattenberg · 2023
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X-pruner: explainable pruning for vision transformers
Lu Yu and Wei Xiang · 2023
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eX-ViT: A novel explainable vision transformer for weakly supervised semantic segmentation
Lu Yu, Wei Xiang, Juan Fang, Yi-Ping Phoebe Chen, and Lianhua Chi · 2023
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Explainability for large language models: A survey
Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, and Mengnan Du · 2023
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