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Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts.
An analysis of approximations for maximizing submodular set functions—i
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher · 1978
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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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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Convex analysis and optimization with submodular functions: a tutorial
Francis Bach · 2010
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Attribute learning in large-scale datasets
Olga Russakovsky and Li Fei-Fei · 2010
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell · 2016
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” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 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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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
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Grounding visual explanations
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata · 2018
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Textual explanations for self-driving vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, and Zeynep Akata · 2018
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Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2018
Cited alongside, same era.
Few-shot learning with graph neural networks
Victor Garcia Satorras and Joan Bruna Estrach · 2018
Cited alongside, same era.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
Cited alongside, same era.
This looks like that: deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su · 2019
Cited alongside, same era.
Darpa’s explainable artificial intelligence (xai) program
David Gunning and David Aha · 2019
Cited alongside, same era.
Visualbert: A simple and performant baseline for vision and language
Revisiting document representations for large-scale zero-shot learning
Jihyung Kil and Wei-Lun Chao · 2021
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Open world compositional zero-shot learning
Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, and Zeynep Akata · 2021
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Do concept bottleneck models learn as intended?
Andrei Margeloiu, Matthew Ashman, Umang Bhatt, Yanzhi Chen, Mateja Jamnik, and Adrian Weller · 2021
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Neural prototype trees for interpretable fine-grained image recognition
Meike Nauta, Ron van Bree, and Christin Seifert · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 2019
Cited alongside, same era.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Cited alongside, same era.
Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal · 2019
Cited alongside, same era.
Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu · 2020
Cited alongside, same era.
How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig · 2020
Cited alongside, same era.
Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze · 2021
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Can language models be biomedical knowledge bases?
Mujeen Sung, Jinhyuk Lee, Sean Yi, Minji Jeon, Sungdong Kim, and Jaewoo Kang · 2021
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Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
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A survey on neural network interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis, and Ke Tang · 2021
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Learning to compose diversified prompts for image emotion classification
Sinuo Deng, Lifang Wu, Ge Shi, Lehao Xing, and Meng Jian · 2022
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Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Supporting vision-language model inference with causality-pruning knowledge prompt
Jiangmeng Li, Wenyi Mo, Wenwen Qiang, Bing Su, and Changwen Zheng · 2022
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Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2022
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Improving few-shot image classification using machine-and user-generated natural language descriptions
Kosuke Nishida, Kyosuke Nishida, and Shuichi Nishioka · 2022
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What does a platypus look like? generating customized prompts for zero-shot image classification
Sarah Pratt, Rosanne Liu, and Ali Farhadi · 2022
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Denseclip: Language-guided dense prediction with context-aware prompting
Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu · 2022
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Integrating language guidance into vision-based deep metric learning
Karsten Roth, Oriol Vinyals, and Zeynep Akata · 2022
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Concept bottleneck model with additional unsupervised concepts
Yoshihide Sawada and Keigo Nakamura · 2022
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K-lite: Learning transferable visual models with external knowledge
Sheng Shen, Chunyuan Li, Xiaowei Hu, Yujia Xie, Jianwei Yang, Pengchuan Zhang, Anna Rohrbach, Zhe Gan, Lijuan Wang, Lu Yuan, et al · 2022
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Explaining patterns in data with language models via interpretable autoprompting
Chandan Singh, John X Morris, Jyoti Aneja, Alexander M Rush, and Jianfeng Gao · 2022
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Post-hoc concept bottleneck models
Mert Yuksekgonul, Maggie Wang, and James Zou · 2022
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Do vision-language pretrained models learn primitive concepts?
Tian Yun, Usha Bhalla, Ellie Pavlick, and Chen Sun · 2022
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Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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