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Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks.
Modeling annotators: A generative approach to learning from annotator rationales
Omar Zaidan and Jason Eisner · 2008
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Imagenet auto-annotation with segmentation propagation
Matthieu Guillaumin, Daniel Küttel, and Vittorio Ferrari · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 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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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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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 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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Long short-term memory-networks for machine reading
Jianpeng Cheng, Li Dong, and Mirella Lapata · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Visualizing deep convolutional neural networks using natural pre-images
Aravindh Mahendran and Andrea Vedaldi · 2016
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A decomposable attention model for natural language inference
Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 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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Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Explaining convolutional neural networks using softmax gradient layer-wise relevance propagation
Brian Kenji Iwana, Ryohei Kuroki, and Seiichi Uchida · 2019
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RoBERTa: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Explaining explanations in ai
Brent Mittelstadt, Chris Russell, and Sandra Wachter · 2019
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Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Understanding individual decisions of cnns via contrastive backpropagation
Jindong Gu, Yinchong Yang, and Volker Tresp · 2018
Cited alongside, same era.
Tell me where to look: Guided attention inference network
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu · 2018
Cited alongside, same era.
Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
Cited alongside, same era.
Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf, and Seong-Whan Lee · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Full-gradient representation for neural network visualization
Suraj Srinivas and François Fleuret · 2019
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Vl-bert: Pre-training of generic visual-linguistic representations
Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai · 2019
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Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal · 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
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Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
Closest in time.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, and Ilya Sutskever · 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 · 2020
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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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