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Label-free model evaluation, or AutoEval, estimates model accuracy on unlabeled test sets, and is critical for understanding model behaviors in various unseen environments.
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Yuval Eldar, Michael Lindenbaum, Moshe Porat, and Yehoshua Y Zeevi · 1997
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Gradient-based learning applied to document recognition
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The challenge of developing statistical reasoning
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Integrating structured biological data by kernel maximum mean discrepancy
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A kernel method for the two-sample-problem
Bernhard Schölkopf, John Platt, and Thomas Hofmann · 2007
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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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The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
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Distribution-free distribution regression
Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, and Larry Wasserman · 2013
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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From word embeddings to document distances
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Unsupervised risk estimation using only conditional independence structure
Jacob Steinhardt and Percy S Liang · 2016
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Network dissection: Quantifying interpretability of deep visual representations
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Estimating accuracy from unlabeled data: A probabilistic logic approach
Emmanouil A Platanios, Hoifung Poon, Tom M Mitchell, and Eric Horvitz · 2017
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
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The frechet distance of training and test distribution predicts the generalization gap
Julian Zilly, Hannes Zilly, Oliver Richter, Roger Wattenhofer, Andrea Censi, and Emilio Frazzoli · 2019
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Computing the testing error without a testing set
Ciprian A Corneanu, Sergio Escalera, and Aleix M Martinez · 2020
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In search of robust measures of generalization
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, and Daniel M Roy · 2020
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Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Analyzing computer vision data-the good, the bad and the ugly
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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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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Predicting the generalization gap in deep networks with margin distributions
Yiding Jiang, Dilip Krishnan, Hossein Mobahi, and Samy Bengio · 2018
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So-net: Self-organizing network for point cloud analysis
Jiaxin Li, Ben M Chen, and Gim Hee Lee · 2018
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Rep the set: Neural networks for learning set representations
Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, and Michalis Vazirgiannis · 2020
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Neural data server: A large-scale search engine for transfer learning data
Xi Yan, David Acuna, and Sanja Fidler · 2020
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Detecting errors and estimating accuracy on unlabeled data with self-training ensembles
Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, and Somesh Jha · 2021
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What does rotation prediction tell us about classifier accuracy under varying testing environments?
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Are labels always necessary for classifier accuracy evaluation?
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Predicting with confidence on unseen distributions
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Set representation learning with generalized sliced-wasserstein embeddings
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Deep stable learning for out-of-distribution generalization
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