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Entropy minimization (EM) is frequently used to increase the accuracy of classification models when they're faced with new data at test time.
On information and sufficiency
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Imagenet: A large-scale hierarchical image database
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An image is worth 16x16 words: Transformers for image recognition at scale. arxiv 2020
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
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Generalisation in humans and deep neural networks
Geirhos, R., Temme, C. R., Rauber, J., Schütt, H. H., Bethge, M., and Wichmann, F. A · 2018
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Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
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Realistic evaluation of semi-supervised learning algortihms
Oliver, A., Odena, A., Raffel, C., Cubuk, E., and Goodfellow, I · 2018
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An empirical study of example forgetting during deep neural network learning
Toneva, M., Sordoni, A., Combes, R. T. d., Trischler, A., Bengio, Y., and Gordon, G. J · 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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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 2019
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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Unsupervised domain adaptation through self-supervision
Sun, Y., Tzeng, E., Darrell, T., and Efros, A. A · 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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Evaluating prediction-time batch normalization for robustness under covariate shift
Nado, Z., Padhy, S., Sculley, D., D’Amour, A., Lakshminarayanan, B., and Snoek, J · 2020
Test time adaptation via conjugate pseudo-labels
Goyal, S., Sun, M., Raghunathan, A., and Kolter, J. Z · 2022
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Evaluating model robustness to patch perturbations
Gu, J., Tresp, V., and Qin, Y · 2022
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3d common corruptions and data augmentation
Kar, O. F., Yeo, T., Atanov, A., and Zamir, A · 2022
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Efficient test-time model adaptation without forgetting
Niu, S., Wu, J., Zhang, Y., Chen, Y., Zheng, S., Zhao, P., and Tan, M · 2022
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Imagenet-d: A new challenging robustness dataset inspired by domain adaptation
Rusak, E., Schneider, S., Gehler, P. V., Bringmann, O., Brendel, W., and Bethge, M · 2022
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Imagenet-cartoon and imagenet-drawing: two domain shift datasets for imagenet
Salvador, T. and Oberman, A. M · 2022
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Prevalence of neural collapse during the terminal phase of deep learning training
Papyan, V., Han, X., and Donoho, D. L · 2020
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A simple way to make neural networks robust against diverse image corruptions
Rusak, E., Schott, L., Zimmermann, R. S., Bitterwolf, J., Bringmann, O., Bethge, M., and Brendel, W · 2020
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Improving robustness against common corruptions by covariate shift adaptation
Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., and Bethge, M · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C. A., Cubuk, E. D., Kurakin, A., and Li, C.-L · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., and Hardt, M · 2020
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Tent: Fully test-time adaptation by entropy minimization
Wang, D., Shelhamer, E., Liu, S., Olshausen, B., and Darrell, T · 2020
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Noise or signal: The role of image backgrounds in object recognition
Xiao, K., Engstrom, L., Ilyas, A., and Madry, A · 2020
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Id and ood performance are sometimes inversely correlated on real-world datasets
Teney, D., Lin, Y., Oh, S. J., and Abbasnejad, E · 2022
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Maxvit: Multi-axis vision transformer
Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., and Li, Y · 2022
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Continual test-time domain adaptation
Wang, Q., Fink, O., Van Gool, L., and Dai, D · 2022
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Reverse engineering self-supervised learning
Ben-Shaul, I., Shwartz-Ziv, R., Galanti, T., Dekel, S., and LeCun, Y · 2023
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In or out? fixing imagenet out-of-distribution detection evaluation
Bitterwolf, J., Müller, M., and Hein, M · 2023
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Santa: Source anchoring network and target alignment for continual test time adaptation
Chakrabarty, G., Sreenivas, M., and Biswas, S · 2023
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Beyond entropy: Style transfer guided single image continual test-time adaptation
Cho, Y., Kim, Y., and Lee, D · 2023
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Reliable test-time adaptation via agreement-on-the-line
Kim, E., Sun, M., Raghunathan, A., and Kolter, Z · 2023
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Predicting out-of-distribution error with confidence optimal transport
Lu, Y., Wang, Z., Zhai, R., Kolouri, S., Campbell, J., and Sycara, K · 2023
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Towards stable test-time adaptation in dynamic wild world
Niu, S., Wu, J., Zhang, Y., Wen, Z., Chen, Y., Zhao, P., and Tan, M · 2023
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Imagenet-patch: A dataset for benchmarking machine learning robustness against adversarial patches
Pintor, M., Angioni, D., Sotgiu, A., Demetrio, L., Demontis, A., Biggio, B., and Roli, F · 2023
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Rdumb: A simple approach that questions our progress in continual test-time adaptation
Press, O., Schneider, S., Kümmerer, M., and Bethge, M · 2023
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Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization
Song, J., Lee, J., Kweon, I. S., and Choi, S · 2023
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Benchmarking robustness to adversarial image obfuscations
Stimberg, F., Chakrabarti, A., Lu, C.-T., Hazimeh, H., Stretcu, O., Qiao, W., Liu, Y., Kaya, M., Rashtchian, C., Fuxman, A., et al · 2023
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Taesiri, M. R., Nguyen, G., Habchi, S., Bezemer, C.-P., and Nguyen, A · 2023
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Döbler, M., Marencke, F., Marsden, R. A., and Yang, B · 2024
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Universal test-time adaptation through weight ensembling, diversity weighting, and prior correction
Marsden, R. A., Döbler, M., and Yang, B · 2024
Closest in time.