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In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy.
Measures of complexity: a nonexhaustive list
Seth Lloyd · 2001
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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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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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Nonaccidental properties underlie human categorization of complex natural scenes
Dirk B. Walther and Dan Shen · 2014
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A meta-analysis of the anomaly detection problem
Andrew Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, and Weng-Keen Wong · 2015
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Inceptionism: Going deeper into neural networks, 2015
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015
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Posterior calibration and exploratory analysis for natural language processing models
Khanh Nguyen and Brendan O’Connor · 2015
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Posterior calibration and exploratory analysis for natural language processing models
Khanh Nguyen and Brendan T. O’Connor · 2015
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LSUN: construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Improved regularization of convolutional neural networks with Cutout
Terrance Devries and Graham W. Taylor · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann Dauphin, and David Lopez-Paz · 2017
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Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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AutoAugment: Learning augmentation policies from data
Ekin Dogus Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le · 2018
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Learning confidence for out-of-distribution detection in neural networks
Terrance Devries and Graham W. Taylor · 2018
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2018
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Improving robustness without sacrificing accuracy with patch Gaussian augmentation
Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin Dogus Cubuk · 2019
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Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Data augmentation using random image cropping and patching for deep cnns
Ryo Takahashi, Takashi Matsubara, and Kuniaki Uehara · 2019
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A fourier perspective on model robustness in computer vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin Dogus Cubuk, and Justin Gilmer · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Between-class learning for image classification
Yuji Tokozume, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
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Machine learning for autonomous driving, 2019
Drago Anguelov · 2019
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An empirical evaluation on robustness and uncertainty of regularization methods
Sanghyuk Chun, Seong Joon Oh, Sangdoo Yun, Dongyoon Han, Junsuk Choe, and Youngjoon Yoo · 2019
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Pre-training without natural images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, and Yutaka Satoh · 2020
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A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2020
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang · 2020
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Learning to see by looking at noise
Manel Baradad, Jonas Wulff, Tongzhou Wang, Phillip Isola, and Antonio Torralba · 2021
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Dina Bashkirova, Dan Hendrycks, Donghyun Kim, Samarth Mishra, Kate Saenko, Kuniaki Saito, Piotr Teterwak, and Ben Usman · 2021
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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 · 2021
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Unsolved problems in ml safety
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Wei hua Hu, Michihiro Yasunaga, Richard L. Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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On interaction between augmentations and corruptions in natural corruption robustness
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Can vision transformers learn without natural images?
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A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R Kauffmann, Robert A Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G Dietterich, and Klaus-Robert Müller · 2021
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