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The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training.
Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement
Schmidhuber, J., Zhao, J., and Wiering, M · 1997
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Learning to Learn
Thrun, S. and Pratt, L. (eds.) · 1998
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Domain transfer multiple kernel learning
Duan, L., Tsang, I.-W., and Xu, D · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G.-E · 2012
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Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2014
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Cnn features off-the-shelf: An astounding baseline for recognition
Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V · 2015
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Domain generalization for object recognition with multi-task autoencoders
Ghifary, M., Kleijn, W. B., Zhang, M., and Balduzzi, D · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M · 2015
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A unified perspective on multi-domain and multi-task learning
Yang, Y. and Hospedales, T · 2015
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Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., and Erhan, D · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 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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Multiclass domain generalization
Deshmukh, A. A., Sharma, S., Cutler, J. W., and Scott, C · 2017
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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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Learning to learn: Meta-critic networks for sample efficient learning
Sung, F., Zhang, L., Xiang, T., Hospedales, T., and Yang, Y · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Metareg: towards domain generalization using meta-regularization
Balaji, Y., Sankaranarayanan, S., and Chellappa, R · 2018
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On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
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Efficient parametrization of multi-domain deep neural networks
Rebuffi, S.-A., Bilen, H., and Vedaldi, A · 2018
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On the convergence of adam and beyond
Reddi, S. J., Kale, S., and Kumar, S · 2018
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Deep value networks learn to evaluate and iteratively refine structured outputs
Gygli, M., Norouzi, M., and Angelova, A · 2017
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Unified deep supervised domain adaptation and generalization
Motiian, S., Piccirilli, M., Adjeroh, D.-A., and Doretto, G · 2017
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Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
Cited alongside, same era.
Learning multiple visual domains with residual adapters
Rebuffi, S.-A., Bilen, H., and Vedaldi, A · 2017
Cited alongside, same era.
Asymmetric tri-training for unsupervised domain adaptation
Saito, K., Ushiku, Y., and Harada, T · 2017
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Prototypical networks for few shot learning
Snell, J., Swersky, K., and Zemel, R. S · 2017
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Incremental learning through deep adaptation
Rosenfeld, A. and Tsotsos, J · 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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A dirt-t approach to unsupervised domain adaptation
Shu, R., Bui, H. H., Narui, H., and Ermon, S · 2018
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Learning to compare: relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P., and Hospedales, T · 2018
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Generalizing to unseen domains via adversarial data augmentation
Volpi, R., Namkoong, H., Sener, O., Duchi, J., Murino, V., and Savarese, S · 2018
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Meta-gradient reinforcement learning
Xu, Z., Hasselt, H., and Silver, D · 2018
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