Abstract
Adversarial domain adaptation has made tremendous success by learning domain-invariant feature representations. However, conventional adversarial training pushes two domains together and brings uncertainty to feature learning, which deteriorates the discriminability in the target domain. In this paper, we tackle this problem by designing a simple yet effective scheme, namely Asymmetric Adversarial Domain Adaptation (AADA). We notice that source features preserve great feature discriminability due to full supervision, and therefore a novel asymmetric training scheme is designed to keep the source features fixed and encourage the target features approaching to the source features, which best preserves the feature discriminability learned from source labeled data. This is achieved by an autoencoder-based domain discriminator that only embeds the source domain, while the feature extractor learns to deceive the autoencoder by embedding the target domain. Theoretical justifications corroborate that our method minimizes the domain discrepancy and spectral analysis is employed to quantize the improved feature discriminability. Extensive experiments on several benchmarks validate that our method outperforms existing adversarial domain adaptation methods significantly and demonstrates robustness with respect to hyper-parameter sensitivity.
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References
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Mach. Learn. 79(1–2), 151–175 (2010). https://doi.org/10.1007/s10994-009-5152-4
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3722–3731 (2017)
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: Advances in Neural Information Processing Systems, pp. 343–351 (2016)
Cao, Z., Ma, L., Long, M., Wang, J.: Partial adversarial domain adaptation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 135–150 (2018)
Chen, X., Wang, S., Long, M., Wang, J.: Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, 09–15 June 2019, Long Beach, California, USA, vol. 97, pp. 1081–1090. PMLR (2019)
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)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
French, G., Mackiewicz, M., Fisher, M.: Self-ensembling for visual domain adaptation. In: International Conference on Learning Representations, no. 6 (2018)
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, Lille, France, 07–09 July 2015, vol. 37, pp. 1180–1189. PMLR (2015)
Ganin, Y.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: Deep reconstruction-classification networks for unsupervised domain adaptation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 597–613. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_36
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
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)
Hoffman, J., et al.: CyCADA: cycle-consistent adversarial domain adaptation. In: Proceedings of the 35th International Conference on Machine Learning, pp. 1989–1998 (2018)
Hsu, Y.C., Lv, Z., Kira, Z.: Learning to cluster in order to transfer across domains and tasks. In: International Conference on Learning Representations (ICLR) (2018)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., Huang, F.: A tutorial onenergy-based learning. Predicting Struct. Data 1 (2006)
Li, Z., Zhang, Y., Wei, Y., Wu, Y., Yang, Q.: End-to-end adversarial memory network for cross-domain sentiment classification. In: IJCAI, pp. 2237–2243 (2017)
Liang, J., He, R., Sun, Z., Tan, T.: Distant supervised centroid shift: a simple and efficient approach to visual domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2975–2984 (2019)
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)
Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 469–477 (2016)
Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, pp. 97–105. ICML 2015 (2015)
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems (2018)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2011)
Pan, S.J., Yang, Q., et al.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Ranzato, M., Boureau, Y.L., Chopra, S., LeCun, Y.: A unified energy-based framework for unsupervised learning. In: Artificial Intelligence and Statistics, pp. 371–379 (2007)
Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2988–2997. JMLR. org (2017)
Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3723–3732 (2018)
Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: The European Conference on Computer Vision, ECCV, September 2018
Sankaranarayanan, S., Balaji, Y., Castillo, C.D., Chellappa, R.: Generate to adapt: aligning domains using generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8503–8512 (2018)
Shu, R., Bui, H.H., Narui, H., Ermon, S.: A dirt-t approach to unsupervised domain adaptation. In: Proceedings 6th International Conference on Learning Representations (2018)
Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_35
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4068–4076 (2015)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 4 (2017)
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance. CoRR abs/1412.3474 http://arxiv.org/abs/1412.3474 (2014)
Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings CVPR, pp. 5018–5027 (2017)
Vu, T.H., Jain, H., Bucher, M., Cord, M., Perez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern RecognitionCVPR, June 2019
Xie, S., Zheng, Z., Chen, L., Chen, C.: Learning semantic representations for unsupervised domain adaptation. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, Stockholmsmässan, Stockholm Sweden, 10–15 July 2018, vol. 80, pp. 5423–5432. PMLR (2018)
Xu, R., Li, G., Yang, J., Lin, L.: Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1426–1435 (2019)
Yang, J., Zou, H., Cao, S., Chen, Z., Xie, L.: Mobileda: towards edge domain adaptation. IEEE Internet Things J. 7, 6909–6918 (2020)
Zhao, J., Mathieu, M., LeCun, Y.: Energy-based generative adversarial network. In: Proceedings 5th International Conference on Learning Representations (2017)
Zhou, X., Karpur, A., Gan, C., Luo, L., Huang, Q.: Unsupervised domain adaptation for 3D keypoint estimation via view consistency. In: ECCV, pp. 137–153 (2018)
Zou, H., Yang, J., Zhou, Y., Xie, L., Spanos, C.J.: Robust wifi-enabled device-free gesture recognition via unsupervised adversarial domain adaptation. In: 2018 27th International Conference on Computer Communication and Networks (ICCCN), pp. 1–8. IEEE (2018)
Zou, H., Zhou, Y., Yang, J., Liu, H., Das, H.P., Spanos, C.J.: Consensus adversarial domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5997–6004 (2019)
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Yang, J., Zou, H., Zhou, Y., Zeng, Z., Xie, L. (2020). Mind the Discriminability: Asymmetric Adversarial Domain Adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_35
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