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Distribution shift is a major source of failure for machine learning models.
“The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization”, 2020
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 · 2006
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“Cats and dogs”
Omkar Parkhi, Andrea Vedaldi, Andrew Zisserman and CV Jawahar · 2012
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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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“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, Alexander. Berg and Li Fei-Fei · 2015
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“Recognition in terra incognita”
Sara Beery, Grant Van and Pietro Perona · 2018
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“Functional Map of the World”
Gordon Christie, Neil Fendley, James Wilson and Ryan Mukherjee · 2018
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“SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications”
Abdullah Hamdi, Matthias Muller and Bernard Ghanem · 2018
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“Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects”
Michael Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku and Anh Nguyen · 2019
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“ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models”
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum and Boris Katz · 2019
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“Exploring the Landscape of Spatial Robustness”
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt and Aleksander Madry · 2019
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“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”
Dan Hendrycks and Thomas. Dietterich · 2019
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“Towards Analyzing Semantic Robustness of Deep Neural Networks”
Abdullah Hamdi and Bernard Ghanem · 2019
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“Do ImageNet Classifiers Generalize to ImageNet?”
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt and Vaishaal Shankar · 2019
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“Learning robust global representations by penalizing local predictive power”
Haohan Wang, Songwei Ge, Eric Xing and Zachary Lipton · 2019
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“PyTorch Image Models”
Ross Wightman · 2019
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“Identifying Statistical Bias in Dataset Replication”
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt and Aleksander Madry · 2020
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Lakshya Jain, Varun Chandrasekaran, Uyeong Jang, Wilson Wu, Andrew Lee, Andy Yan, Steven Chen, Somesh Jha and Sanjit. Seshia · 2020
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“Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization”
Shiori Sagawa, Pang Koh, Tatsunori. Hashimoto and Percy Liang · 2020
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“Adaptive Testing of Computer Vision Models”
Irena Gao, Gabriel Ilharco, Scott Lundberg and Marco Ribeiro · 2022
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“Distilling Model Failures as Directions in Latent Space”
Saachi Jain, Hannah Lawrence, Ankur Moitra and Aleksander Madry · 2022
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“Diffusion Models for Counterfactual Explanations”
Guillaume Jeanneret, Loïc Simon and Frédéric Jurie · 2022
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“Invariant Learning via Diffusion Dreamed Distribution Shifts”
Priyatham Kattakinda, Alexander Levine and Soheil Feizi · 2022
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“Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance”
Aengus Lynch, Jean Kaddour and Ricardo Silva · 2022
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“Identifying Model Weakness with Adversarial Examiner”
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“Measuring Robustness to Natural Distribution Shifts in Image Classification”
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht and Ludwig Schmidt · 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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“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 and Greg Yang · 2021
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“Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization”
John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Koh, Vaishaal Shankar, Percy Liang, Yair Carmon and Ludwig Schmidt · 2021
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“Learning transferable visual models from natural language supervision”
Alec Radford, Jong Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin and Jack Clark · 2021
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“Clip Retrieval: Easily compute clip embeddings and build a clip retrieval system with them”
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“High-resolution image synthesis with latent diffusion models”
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“Hierarchical text-conditional image generation with clip latents”
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu and Mark Chen · 2022
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“Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation”
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“Laion-5b: An open large-scale dataset for training next generation image-text models”
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis and Mitchell Wortsman · 2022
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“Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding”
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Ghasemipour, Burcu Ayan, S Mahdavi and Rapha Lopes · 2022
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“Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning”
Olivia Wiles, Isabela Albuquerque and Sven Gowal · 2022
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Jianhao Yuan, Francesco Pinto, Adam Davies, Aarushi Gupta and Philip Torr · 2022
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