Fetching the paper…
Reading the bibliography…
Theoretically, domain adaptation is a well-researched problem.
Bengio, S., Bengio, Y., Cloutier, J., Gecsei, J.: On the optimization of a synaptic learning rule. In: Preprints Conf. Optimality in Artificial and Biological Neural Networks, vol. 2 (1992). Univ. of Texas
1992
Earlier work this paper cites.
Kullback, S.: Information Theory and Statistics. Courier Corporation, ??? (1997)
1997
Earlier work this paper cites.
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, ??? (1999)
1999
Earlier work this paper cites.
Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: VLDB, vol. 4, pp. 180–191 (2004)
2004
Earlier work this paper cites.
Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: Advances in Neural Information Processing Systems, pp. 137–144 (2007)
2007
Earlier work this paper cites.
Crammer, K., Kearns, M., Wortman, J.: Learning from multiple sources. In: Advances in Neural Information Processing Systems, pp. 321–328 (2007)
2007
Earlier work this paper cites.
Sugiyama, M., Krauledat, M., Müller, K.-R.: Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research 8
2007
Earlier work this paper cites.
Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation with multiple sources. In: Advances in Neural Information Processing Systems, pp. 1041–1048 (2009)
2009
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: CVPR (2009)
2009
Earlier work this paper cites.
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Machine learning 79
2010
Earlier work this paper cites.
Ben-David, S., Lu, T., Luu, T., Pal, D.: Impossibility theorems for domain adaptation. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 9, pp. 129–136. PMLR, Chia Laguna Resort, Sardinia, Italy (2010). https://proceedings.mlr.press/v9/david10a.html
2010
Earlier work this paper cites.
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
2010
Earlier work this paper cites.
Blanchard, G., Lee, G., Scott, C.: Generalizing from several related classification tasks to a new unlabeled sample. Advances in neural information processing systems 24
2011
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.
Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. The Journal of Machine Learning Research 13
2012
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.
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189 (2015)
2015
Earlier work this paper cites.
Yosinski, J., Clune, J., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. In: In ICML Workshop on Deep Learning (2015). Citeseer
2015
Earlier work this paper cites.
Wright, S.: Chapter 2 (from an upcoming textbook). In: IMA New Directions Workshop on Mathematical Optimization, p. 20 (2016). http://pages.cs.wisc.edu/ swright/nd2016/chapter2.pdf
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Cited alongside, same era.
2017
Cited alongside, same era.
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.
Liu, H., Long, M., Wang, J., Jordan, M.: Transferable adversarial training: A general approach to adapting deep classifiers. In: International Conference on Machine Learning, pp. 4013–4022 (2019)
2019
Later among the works it cites.
Schoenauer-Sebag, A., Heinrich, L., Schoenauer, M., Sebag, M., Wu, L.F., Altschuler, S.J.: Multi-domain adversarial learning. In: International Conference on Learning Representation (2019)
2019
Later among the works it cites.
Zhang, Y., Liu, T., Long, M., Jordan, M.: Bridging theory and algorithm for domain adaptation. In: International Conference on Machine Learning, pp. 7404–7413 (2019)
2019
Later among the works it cites.
Kuroki, S., Charoenphakdee, N., Bao, H., Honda, J., Sato, I., Sugiyama, M.: Unsupervised domain adaptation based on source-guided discrepancy. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4122–4129 (2019)
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Redko, I., Habrard, A., Sebban, M.: Theoretical analysis of domain adaptation with optimal transport. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 737–753 (2017). Springer
2017
Cited alongside, same era.
Li, Y., Tian, X., Gong, M., Liu, Y., Liu, T., Zhang, K., Tao, D.: Deep domain generalization via conditional invariant adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 624–639 (2018)
2018
Cited alongside, same era.
Li, H., Jialin Pan, S., Wang, S., Kot, A.C.: Domain generalization with adversarial feature learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5400–5409 (2018)
2018
Cited alongside, same era.
Zhao, H., Zhang, S., Wu, G., Moura, J.M., Costeira, J.P., Gordon, G.J.: Adversarial multiple source domain adaptation. In: Advances in Neural Information Processing Systems, pp. 8559–8570 (2018)
2018
Cited alongside, same era.
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
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.
You, K., Wang, X., Long, M., Jordan, M.: Towards accurate model selection in deep unsupervised domain adaptation. In: International Conference on Machine Learning, pp. 7124–7133 (2019). PMLR
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.
Wang, H., He, Z., Lipton, Z.L., Xing, E.P.: Learning robust representations by projecting superficial statistics out. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=rJEjjoR9K7
2019
Later among the works it cites.
Carlucci, F.M., D’Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving jigsaw puzzles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2229–2238 (2019)
2019
Later among the works it cites.
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., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc., ??? (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
2019
Later among the works it cites.
Matsuura, T., Harada, T.: Domain generalization using a mixture of multiple latent domains. In: AAAI (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Tachet des Combes, R., Zhao, H., Wang, Y.-X., Gordon, G.J.: Domain adaptation with conditional distribution matching and generalized label shift. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 19276–19289. Curran Associates, Inc., ??? (2020). https://proceedings.neurips.cc/paper/2020/file/dfbfa7ddcfffeb581f50edcf9a0204bb-Paper.pdf
2020
Later among the works it cites.
Germain, P., Habrard, A., Laviolette, F., Morvant, E.: Pac-bayes and domain adaptation. Neurocomputing 379
2020
Later among the works it cites.
2020
Later among the works it cites.
Gulrajani, I., Lopez-Paz, D.: In search of lost domain generalization. In: International Conference on Learning Representations (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Biewald, L.: Experiment Tracking with Weights and Biases. Software available from wandb.com (2020). https://www.wandb.com/
2020
Later among the works it cites.
Blanchard, G., Deshmukh, A.A., Dogan, Ü., Lee, G., Scott, C.: Domain generalization by marginal transfer learning. J. Mach. Learn. Res. 22
2021
Closest in time.