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Analyzing model performance in various unseen environments is a critical research problem in the machine learning community.
The proof and measurement of association between two things
Charles Spearman · 1961
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Domain generalization for object recognition with multi-task autoencoders
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi · 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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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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A comprehensive survey on domain adaptation for visual applications
Gabriela Csurka · 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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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Cleannet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Do CIFAR-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
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Real-world noisy image denoising: A new benchmark
Jun Xu, Hui Li, Zhetong Liang, David Zhang, and Lei Zhang · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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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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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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SELFIE: Refurbishing unclean samples for robust deep learning
Hwanjun Song, Minseok Kim, and Jae-Gil Lee · 2019
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Reducing domain gap by reducing style bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo · 2021
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Gradient starvation: A learning proclivity in neural networks
Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C Courville, Doina Precup, and Guillaume Lajoie · 2021
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Label-free model evaluation with semi-structured dataset representations
Xiaoxiao Sun, Yunzhong Hou, Hongdong Li, and Liang Zheng · 2021
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Learning with noisy labels revisited: A study using real-world human annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu · 2021
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Agreement-on-the-line: Predicting the performance of neural networks under distribution shift
Christina Baek, Yiding Jiang, Aditi Raghunathan, and J Zico Kolter · 2022
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Harder or different? a closer look at distribution shift in dataset reproduction
Shangyun Lu, Bradley Nott, Aaron Olson, Alberto Todeschini, Hossein Vahabi, Yair Carmon, and Ludwig Schmidt · 2020
Cited alongside, same era.
Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2020
Cited alongside, same era.
The iwildcam 2021 competition dataset
Sara Beery, Arushi Agarwal, Elijah Cole, and Vighnesh Birodkar · 2021
Cited alongside, same era.
Are labels always necessary for classifier accuracy evaluation?
Weijian Deng and Liang Zheng · 2021
Cited alongside, same era.
Do we really need gold samples for sample weighting under label noise?
Aritra Ghosh and Andrew Lan · 2021
Cited alongside, same era.
Predicting with confidence on unseen distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, and Ludwig Schmidt · 2021
Cited alongside, same era.
Selfreg: Self-supervised contrastive regularization for domain generalization
Daehee Kim, Youngjun Yoo, Seunghyun Park, Jinkyu Kim, and Jaekoo Lee · 2021
Cited alongside, same era.
Probable domain generalization via quantile risk minimization
Cian Eastwood, Alexander Robey, Shashank Singh, Julius Von Kügelgen, Hamed Hassani, George J Pappas, and Bernhard Schölkopf · 2022
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Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, and Hanie Sedghi · 2022
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Grounding visual representations with texts for domain generalization
Seonwoo Min, Nokyung Park, Siwon Kim, Seunghyun Park, and Jinkyu Kim · 2022
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Fishr: Invariant gradient variances for out-of-distribution generalization
Alexandre Rame, Corentin Dancette, and Matthieu Cord · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Domain generalization by joint-product distribution alignment
Sentao Chen, Lei Wang, Zijie Hong, and Xiaowei Yang · 2023
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Confidence and dispersity speak: Characterising prediction matrix for unsupervised accuracy estimation
Weijian Deng, Yumin Suh, Stephen Gould, and Liang Zheng · 2023
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Vne: An effective method for improving deep representation by manipulating eigenvalue distribution
Jaeill Kim, Suhyun Kang, Duhun Hwang, Jungwook Shin, and Wonjong Rhee · 2023
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Source-free progressive graph learning for open-set domain adaptation
Yadan Luo, Zijian Wang, Zhuoxiao Chen, Zi Huang, and Mahsa Baktashmotlagh · 2023
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A bag-of-prototypes representation for dataset-level applications
Weijie Tu, Weijian Deng, Tom Gedeon, and Liang Zheng · 2023
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