Abstract
Unsupervised domain adaptation has achieved great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target samples are exactly the same as those of the source domain. In this paper, we tackle the more challenging scenario of open set domain adaptation with a novel end-to-end training approach, where the samples of unknown class can be present in the target domain. Our method employs entropy minimization for performing unsupervised domain adaptation, where unknown samples are aggressively used in training by forcing the classifier to output the probability of 0.5 on the unknown class. Experimental evidence demonstrates that our approach significantly outperforms the state-of-the-art in open set domain adaptation.
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References
Baktashmotlagh, M., Faraki, M., Drummond, T., Salzmann, M.: Learning factorized representations for open-set domain adaptation. In: ICLR (2019)
Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: NIPS (2007)
Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Wortman, J.: Learning bounds for domain adaptation. In: NIPS (2008)
Busto, P.P., Gall, J.: Open set domain adaptation. In: ICCV (2017)
Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: ICML (2013)
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: Unsupervised visual domain adaptation using subspace alignment. In: ICCV (2013)
Ganin, Y., Lempitsky, V.S.: Unsupervised domain adaptation by backpropagation. In: ICML (2015)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR (2012)
Haeusser, P., Frerix, T., Mordvintsev, A., Cremers, D.: Associative domain adaptation. In: ICCV (2017)
Ian, G., et al.: Generative adversarial nets. In: NIPS (2014)
Jain, L.P., Scheirer, W.J., Boult, T.E.: Multi-class open set recognition using probability of inclusion. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 393–409. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_26
Konstantinos, B., George, T., Nathan, S., Dilip, K., Dumitru, E.: Domain separation networks. In: NIPS (2016)
Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: ICML (2015)
Chen, M., Weinberger, K.Q., Blitzer, J.: Co-training for domain adaptation. In: NIPS (2011)
Pan, S., Tsang, I., Kwok, J., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22, 199–210 (2011)
Pietro, M., Jacopo, C., Vittorio, M.: Minimal-entropy correlation alignment for unsupervised deep domain adaptation. In: ICLR (2018)
Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: CVPR (2018)
Sun, B., Saenko, K.: Deep coral: correlation alignment for deep domain adaptation. In: ICCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (2016)
Tommasi, T., Tuytelaars, T.: A testbed for cross-dataset analysis. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8927, pp. 18–31. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16199-0_2
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR (2017)
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K.: Deep domain confusion: maximizing for domain invariance. arXiv:1412.3474 (2014)
Wu, X., Zhang, S., Zhou, Q., Yang, Z., Zhao, C., Latecki, L.J.: Minimal-entropy diversity maximization for unsupervised domain adaptation. arXiv:2002.01690 (2020)
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Wu, X., Cheng, L., Zhang, S. (2020). Open Set Domain Adaptation with Entropy Minimization. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12307. Springer, Cham. https://doi.org/10.1007/978-3-030-60636-7_3
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DOI: https://doi.org/10.1007/978-3-030-60636-7_3
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