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Standard training datasets for deep learning often contain objects in common settings (e.g., "a horse on grass" or "a ship in water") since they are usually collected by randomly scraping the web.
The role of context in object recognition
Aude Oliva and Antonio Torralba · 2007
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Object categorization using co-occurrence, location and appearance
Carolina Galleguillos, Andrew Rabinovich, and Serge Belongie · 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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An empirical study of context in object detection
Santosh K. Divvala, Derek Hoiem, James H. Hays, Alexei A. Efros, and Martial Hebert · 2009
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Context models and out-of-context objects
Myung Jin Choi, Antonio Torralba, and Alan S. Willsky · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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The role of context for object detection and semantic segmentation in the wild
Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille · 2014
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Deep learning face attributes in the wild
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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 S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Sean Bell, C. Lawrence Zitnick, Kavita Bala, and Ross Girshick · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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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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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Amir Rosenfeld, Richard Zemel, and John K Tsotsos · 2018
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Distributionally robust neural networks
Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang · 2020
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Detecting out-of-distribution examples with Gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2020
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Noise or signal: The role of image backgrounds in object recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 2020
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Pytorch image models
Ross Wightman · 2019
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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3db: A framework for debugging computer vision models
Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, and Aleksander Madry · 2021
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Learning transferable visual models from natural language supervision
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Counterfactual generative networks
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Understanding failures of deep networks via robust feature extraction
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Leveraging sparse linear layers for debuggable deep networks
Eric Wong, Shibani Santurkar, and Aleksander Madry · 2021
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