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In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.
Using pre-training can improve model robustness and uncertainty
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Multitask learning
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Rapid object detection using a boosted cascade of simple features
Viola, P., Jones, M., et al · 2001
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Fei-Fei, L., Fergus, R., and Perona, P · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Co-training for domain adaptation
Chen, M., Weinberger, K. Q., and Blitzer, J · 2011
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Online domain adaptation of a pre-trained cascade of classifiers
Jain, V. and Learned-Miller, E · 2011
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Tracking-learning-detection
Kalal, Z., Mikolajczyk, K., and Matas, J · 2011
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Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., and Grauman, K · 2012
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Discovering latent domains for multisource domain adaptation
Hoffman, J., Kulis, B., Darrell, T., and Saenko, K · 2012
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Online learning and online convex optimization
Shalev-Shwartz, S. et al · 2012
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Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B · 2013
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The nature of statistical learning theory
Vapnik, V · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Continuous manifold based adaptation for evolving visual domains
Hoffman, J., Darrell, T., and Saenko, K · 2014
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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
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Domain generalization for object recognition with multi-task autoencoders
Ghifary, M., Bastiaan Kleijn, W., Zhang, M., and Balduzzi, D · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M. I · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Learning attributes equals multi-source domain generalization
Gan, C., Yang, T., and Gong, B · 2016
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
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Introduction to online convex optimization
Hazan, E. et al · 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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Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q · 2016
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Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2016
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Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
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Colorful image colorization
Zhang, R., Isola, P., and Efros, A. A · 2016
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Unsupervised learning by predicting noise
Bojanowski, P. and Joulin, A · 2017
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Domain adaptation for visual applications: A comprehensive survey
Csurka, G · 2017
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Rethinking imagenet pre-training
He, K., Girshick, R., and Dollár, P · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Hendrycks, D., Mazeika, M., Wilson, D., and Gimpel, K · 2018
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Algorithms and theory for multiple-source adaptation
Hoffman, J., Mohri, M., and Zhang, N · 2018
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Kannan, H., Kurakin, A., and Goodfellow, I · 2018
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Deep domain generalization via conditional invariant adversarial networks
Li, Y., Tian, X., Gong, M., Liu, Y., Liu, T., Zhang, K., and Tao, D · 2018
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Countering adversarial images using input transformations
Guo, C., Rana, M., Cisse, M., and van der Maaten, L · 2017
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Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., and Darrell, T · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
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Colorization as a proxy task for visual understanding
Larsson, G., Maire, M., and Shakhnarovich, G · 2017
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Learning without forgetting
Li, Z. and Hoiem, D · 2017
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Liu, Z., Sun, M., Zhou, T., Huang, G., and Darrell, T · 2018
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Online model distillation for efficient video inference
Mullapudi, R. T., Chen, S., Zhang, K., Ramanan, D., and Fatahalian, K · 2018
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Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 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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Generalizing across domains via cross-gradient training
Shankar, S., Piratla, V., Chakrabarti, S., Chaudhuri, S., Jyothi, P., and Sarawagi, S · 2018
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“zero-shot” super-resolution using deep internal learning
Shocher, A., Cohen, N., and Irani, M · 2018
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Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2018
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Surprising effectiveness of few-image unsupervised feature learning
Asano, Y. M., Rupprecht, C., and Vedaldi, A · 2019
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Semantic photo manipulation with a generative image prior
Bau, D., Strobelt, H., Peebles, W., Wulff, J., Zhou, B., Zhu, J.-Y., and Torralba, A · 2019
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Domain generalization by solving jigsaw puzzles
Carlucci, F. M., D’Innocente, A., Bucci, S., Caputo, B., and Tommasi, T · 2019
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Certified adversarial robustness via randomized smoothing
Cohen, J. M., Rosenfeld, E., and Kolter, J. Z · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Transfer of adversarial robustness between perturbation types
Kang, D., Sun, Y., Brown, T., Hendrycks, D., and Steinhardt, J · 2019
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Episodic training for domain generalization
Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.-Z., and Hospedales, T. M · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H., Yang, G., Li, J., Zhang, P., Zhang, H., Razenshteyn, I., and Bubeck, S · 2019
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Do image classifiers generalize across time?
Shankar, V., Dave, A., Roelofs, R., Ramanan, D., Recht, B., and Schmidt, L · 2019
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Boosting supervision with self-supervision for few-shot learning
Su, J.-C., Maji, S., and Hariharan, B · 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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Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I · 2019
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