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We introduce Domain-specific Masks for Generalization, a model for improving both in-domain and out-of-domain generalization performance.
Jaccard, P.: Etude de la distribution florale dans une portion des alpes et du jura. Bulletin de la Societe Vaudoise des Sciences Naturelles 37
1901
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the damage of dataset bias. In: European Conference on Computer Vision. pp. 158–171. Springer (2012)
2012
Earlier work this paper cites.
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the damage of dataset bias. In: European Conference on Computer Vision. pp. 158–171. Springer (2012)
2012
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
Muandet, K., Balduzzi, D., Schölkopf, B.: Domain generalization via invariant feature representation. In: International Conference on Machine Learning. pp. 10–18 (2013)
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15
2014
Earlier work this paper cites.
Ghifary, M., Bastiaan Kleijn, W., Zhang, M., Balduzzi, D.: Domain generalization for object recognition with multi-task autoencoders. In: Proceedings of the IEEE international conference on computer vision. pp. 2551–2559 (2015)
2015
Earlier work this paper cites.
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D.: Domain generalization for object recognition with multi-task autoencoders. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015 (2015)
2015
Earlier work this paper cites.
Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: Proceedings of the 32nd International Conference on Machine Learning. pp. 97–105 (2015)
2015
Earlier work this paper cites.
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: Advances in neural information processing systems. pp. 343–351 (2016)
2016
Earlier work this paper cites.
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In: Advances in neural information processing systems. pp. 2172–2180 (2016)
2016
Earlier work this paper cites.
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. The Journal of Machine Learning Research 17
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Rahman, M.A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: International symposium on visual computing. pp. 234–244. Springer (2016)
2016
Cited alongside, same era.
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)
2017
Cited alongside, same era.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 1126–1135. JMLR. org (2017)
2017
Cited alongside, same era.
Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Deeper, broader and artier domain generalization. In: International Conference on Computer Vision (2017)
2017
Cited alongside, same era.
Saito, K., Ushiku, Y., Harada, T., Saenko, K.: Adversarial dropout regularization. In: International Conference on Learning Representations (2018)
2018
Later among the works it cites.
Sankaranarayanan, S., Balaji, Y., Castillo, C.D., Chellappa, R.: Generate to adapt: Aligning domains using generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Later among the works it cites.
Serra, J., Suris, D., Miron, M., Karatzoglou, A.: Overcoming catastrophic forgetting with hard attention to the task. In: International Conference on Machine Learning. pp. 4548–4557 (2018)
2018
Later among the works it cites.
Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., Murino, V., Savarese, S.: Generalizing to unseen domains via adversarial data augmentation. In: Advances in Neural Information Processing Systems. pp. 5334–5344 (2018)
2018
Later among the works it cites.
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Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 5542–5550 (2017)
2017
Cited alongside, same era.
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017. Proceedings of Machine Learning Research, vol. 70, pp. 2208–2217. PMLR (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7167–7176 (2017)
2017
Cited alongside, same era.
Balaji, Y., Sankaranarayanan, S., Chellappa, R.: Metareg: Towards domain generalization using meta-regularization. In: Advances in Neural Information Processing Systems. pp. 998–1008 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
French, G., Mackiewicz, M., Fisher, M.: Self-ensembling for visual domain adaptation. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=rkpoTaxA-
2018
Cited alongside, same era.
Hoffman, J., Tzeng, E., Park, T., Zhu, J., Isola, P., Saenko, K., Efros, A.A., Darrell, T.: Cycada: Cycle-consistent adversarial domain adaptation. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018. pp. 1994–2003 (2018)
2018
Cited alongside, same era.
Xu, R., Chen, Z., Zuo, W., Yan, J., Lin, L.: Deep cocktail network: Multi-source unsupervised domain adaptation with category shift. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3964–3973 (2018)
2018
Later among the works it cites.
2019
Later among the works it cites.
Berriel, R., Lathuillere, S., Nabi, M., Klein, T., Oliveira-Santos, T., Sebe, N., Ricci, E.: Budget-aware adapters for multi-domain learning. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 382–391 (2019)
2019
Later among the works it cites.
Dou, Q., de Castro, D.C., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. In: Advances in Neural Information Processing Systems. pp. 6447–6458 (2019)
2019
Later among the works it cites.
Lee, S., Kim, D., Kim, N., Jeong, S.G.: Drop to adapt: Learning discriminative features for unsupervised domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 91–100 (2019)
2019
Later among the works it cites.
Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.Z., Hospedales, T.M.: Episodic training for domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1446–1455 (2019)
2019
Later among the works it cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in neural information processing systems. pp. 8026–8037 (2019)
2019
Later among the works it cites.
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1406–1415 (2019)
2019
Later among the works it cites.
Peng, X., Huang, Z., Sun, X., Saenko, K.: Domain agnostic learning with disentangled representations. In: ICML (2019)
2019
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2019
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2020
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2020
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