Designing network design spaces
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A survey on domain adaptation theory: learning bounds and theoretical guarantees
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Learning to validate the predictions of black box classifiers on unseen data
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Beit: Bert pre-training of image transformers
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Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2021
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High-performance large-scale image recognition without normalization
Andy Brock, Soham De, Samuel L Smith, and Karen Simonyan · 2021
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Convit: Improving vision transformers with soft convolutional inductive biases
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Are labels always necessary for classifier accuracy evaluation?
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What does rotation prediction tell us about classifier accuracy under varying testing environments?
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Weijian Deng, Stephen Gould, and Liang Zheng · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
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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Ratt: Leveraging unlabeled data to guarantee generalization
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Partial success in closing the gap between human and machine vision
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2021
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Predicting with confidence on unseen distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, and Ludwig Schmidt · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
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Accuracy on the line: On the strong correlation between out-of-distribution and in-distribution generalization
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Distributional generalization: A new kind of generalization, 2021
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Bottleneck transformers for visual recognition
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Going deeper with image transformers
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Scaling local self-attention for parameter efficient visual backbones
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Co-scale conv-attentional image transformers
Weijian Xu, Yifan Xu, Tyler Chang, and Zhuowen Tu · 2021
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Leveraging unlabeled data to predict out-of-distribution performance
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Assessing generalization of SGD via disagreement
Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, and J Zico Kolter · 2022
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Deconstructing distributions: A pointwise framework of learning
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On the nonlinear correlation of ml performance across data subpopulations
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A convnet for the 2020s
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Id and ood performance are sometimes inversely correlated on real-world datasets
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Predicting out-of-distribution error with the projection norm, 2022
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, and Jacob Steinhardt · 2022
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