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This work aims to assess how well a model performs under distribution shifts without using labels.
Rank correlation methods
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Matrix rank minimization with applications
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Co-validation: Using model disagreement on unlabeled data to validate classification algorithms
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Pearson correlation coefficient
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
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Unsupervised supervised learning i: Estimating classification and regression errors without labels
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Discriminative clustering by regularized information maximization
Krause, A., Perona, P., and Gomes, R · 2010
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Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Shi, Y. and Sha, F · 2012
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Machine learning in non-stationary environments: Introduction to covariate shift adaptation
Sugiyama, M. and Kawanabe, M · 2012
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Estimating accuracy from unlabeled data: A bayesian approach
Platanios, E. A., Dubey, A., and Mitchell, T · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Chrabaszcz, P., Loshchilov, I., and Hutter, F · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
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Exploring generalization in deep learning
Neyshabur, B., Bhojanapalli, S., McAllester, D., and Srebro, N · 2017
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Estimating accuracy from unlabeled data: A probabilistic logic approach
Platanios, E., Poon, H., Mitchell, T. M., and Horvitz, E. J · 2017
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Stronger generalization bounds for deep nets via a compression approach
Arora, S., Ge, R., Neyshabur, B., and Zhang, Y · 2018
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Detecting and correcting for label shift with black box predictors
Lipton, Z., Wang, Y.-X., and Smola, A · 2018
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Do cifar-10 classifiers generalize to cifar-10?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2018
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J., and Katz, B · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J · 2019
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Measuring generalization with optimal transport
Chuang, C.-Y., Mroueh, Y., Greenewald, K., Torralba, A., and Jegelka, S · 2021
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Fast batch nuclear-norm maximization and minimization for robust domain adaptation
Cui, S., Wang, S., Zhuo, J., Li, L., Huang, Q., and Tian, Q · 2021
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Are labels always necessary for classifier accuracy evaluation?
Deng, W. and Zheng, L · 2021
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What does rotation prediction tell us about classifier accuracy under varying testing environments?
Deng, W., Gould, S., and Zheng, L · 2021
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Repvgg: Making vgg-style convnets great again
Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J · 2021
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Confidence calibration for domain generalization under covariate shift
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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Learning robust global representations by penalizing local predictive power
Wang, H., Ge, S., Lipton, Z., and Xing, E. P · 2019
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Pytorch image models
Wightman, R · 2019
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Estimating generalization under distribution shifts via domain-invariant representations
Chuang, C.-Y., Torralba, A., and Jegelka, S · 2020
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Computing the testing error without a testing set
Corneanu, C. A., Escalera, S., and Martinez, A. M · 2020
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Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations
Cui, S., Wang, S., Zhuo, J., Li, L., Huang, Q., and Tian, Q · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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Gong, Y., Lin, X., Yao, Y., Dietterich, T. G., Divakaran, A., and Gervasio, M · 2021
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Predicting with confidence on unseen distributions
Guillory, D., Shankar, V., Ebrahimi, S., Darrell, T., and Schmidt, L · 2021
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Calibration of neural networks using splines
Gupta, K., Rahimi, A., Ajanthan, T., Mensink, T., Sminchisescu, C., and Hartley, R · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al · 2021
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Soft calibration objectives for neural networks
Karandikar, A., Cain, N., Tran, D., Lakshminarayanan, B., Shlens, J., Mozer, M. C., and Roelofs, B · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
Miller, J. P., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., and Schmidt, L · 2021
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Revisiting the calibration of modern neural networks
Minderer, M., Djolonga, J., Romijnders, R., Hubis, F., Zhai, X., Houlsby, N., Tran, D., and Lucic, M · 2021
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On interaction between augmentations and corruptions in natural corruption robustness
Mintun, E., Kirillov, A., and Xie, S · 2021
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Predicting deep neural network generalization with perturbation response curves
Schiff, Y., Quanz, B., Das, P., and Chen, P.-Y · 2021
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Fine-grained image analysis with deep learning: A survey
Wei, X.-S., Song, Y.-Z., Mac Aodha, O., Wu, J., Peng, Y., Tang, J., Yang, J., and Belongie, S · 2021
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Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Yang, S., van de Weijer, J., Herranz, L., Jui, S., et al · 2021
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Agreement-on-the-line: Predicting the performance of neural networks under distribution shift
Baek, C., Jiang, Y., Raghunathan, A., and Kolter, Z · 2022
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On the strong correlation between model invariance and generalization
Deng, W., Gould, S., and Zheng, L · 2022
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Leveraging unlabeled data to predict out-of-distribution performance
Garg, S., Balakrishnan, S., Lipton, Z. C., Neyshabur, B., and Sedghi, H · 2022
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A convnet for the 2020s
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
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Prior knowledge guided unsupervised domain adaptation
Sun, T., Lu, C., and Ling, H · 2022
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Attracting and dispersing: A simple approach for source-free domain adaptation
Yang, S., Wang, Y., Wang, K., Jui, S., and van de Weijer, J · 2022
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Predicting out-of-distribution error with the projection norm
Yu, Y., Yang, Z., Wei, A., Ma, Y., and Steinhardt, J · 2022
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