Abstract
The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be used to mitigate this domain shift by learning domain invariant representations. However, in practice, we find that most existing pretext tasks are ineffective against other established techniques. Thus, we theoretically analyze how and when a subsidiary pretext task could be leveraged to assist the goal task of a given DA problem and develop objective subsidiary task suitability criteria. Based on this criteria, we devise a novel process of sticker intervention and cast sticker classification as a supervised subsidiary DA problem concurrent to the goal task unsupervised DA. Our approach not only improves goal task adaptation performance, but also facilitates privacy-oriented source-free DA i.e. without concurrent source-target access. Experiments on the standard Office-31, Office-Home, DomainNet, and VisDA benchmarks demonstrate our superiority for both single-source and multi-source source-free DA. Our approach also complements existing non-source-free works, achieving leading performance.
J. N. Kundu, S. Bhambri and A. Kulkarni—Equal contribution | Webpage: https://sites.google.com/view/sticker-sfda.
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References
Aggarwal, S., Kundu, J.N., Babu, R.V., Chakraborty, A.: WAMDA: weighted alignment of sources for multi-source domain adaptation. In: BMVC (2020)
Ahmed, S.M., Raychaudhuri, D.S., Paul, S., Oymak, S., Roy-Chowdhury, A.K.: Unsupervised multi-source domain adaptation without access to source data. In: CVPR (2021)
Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: NeurIPS (2006)
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: CVPR (2017)
Carlucci, F.M., D’Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving jigsaw puzzles. In: CVPR (2019)
Chen, Y.H., Chen, W.Y., Chen, Y.T., Tsai, B.C., Frank Wang, Y.C., Sun, M.: No more discrimination: cross city adaptation of road scene segmenters. In: ICCV (2017)
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV (2015)
Dong, J., Fang, Z., Liu, A., Sun, G., Liu, T.: Confident anchor-induced multi-source free domain adaptation. In: NeurIPS (2021)
Feng, Z., Xu, C., Tao, D.: Self-supervised representation learning from multi-domain data. In: ICCV (2019)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–2130 (2016)
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D.: Domain generalization for object recognition with multi-task autoencoders. In: ICCV (2015)
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
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: ICLR (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Jiaolong, X., Liang, X., López, A.M.: Self-supervised domain adaptation for computer vision tasks. IEEE Access 7, 156694–156706 (2019)
Kim, D., Saito, K., Oh, T.H., Plummer, B.A., Sclaroff, S., Saenko, K.: CDS: cross-domain self-supervised pre-training. In: ICCV (2021)
Kundu, J.N., et al.: Balancing discriminability and transferability for source-free domain adaptation. In: ICML (2022)
Kundu, J.N., Kulkarni, A., Singh, A., Jampani, V., Babu, R.V.: Generalize then adapt: source-free domain adaptive semantic segmentation. In: ICCV (2021)
Kundu, J.N., Venkat, N., M V, R., Babu, R.V.: Universal source-free domain adaptation. In: CVPR (2020)
Kundu, J.N., Venkat, N., Revanur, A., Rahul, M.V., Babu, R.V.: Towards inheritable models for open-set domain adaptation. In: CVPR (2020)
Kundu, J.N., Venkatesh, R.M., Venkat, N., Revanur, A., Babu, R.V.: Class-incremental domain adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 53–69. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58601-0_4
Larsson, G., Maire, M., Shakhnarovich, G.: Colorization as a proxy task for visual understanding. In: CVPR (2017)
Li, R., Jiao, Q., Cao, W., Wong, H.S., Wu, S.: Model adaptation: unsupervised domain adaptation without source data. In: CVPR (2020)
Li, S., Xie, M., Lv, F., Liu, C.H., Liang, J., Qin, C., Li, W.: Semantic concentration for domain adaptation. In: ICCV (2021)
Li, Y., Yuan, L., Chen, Y., Wang, P., Vasconcelos, N.: Dynamic transfer for multi-source domain adaptation. In: CVPR (2021)
Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In: ICML (2020)
Liang, J., Hu, D., Wang, Y., He, R., Feng, J.: Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer. IEEE Trans. Pattern Anal. Mach. Intell. (2021)
Liu, Y., Zhang, W., Wang, J.: Source-free domain adaptation for semantic segmentation. In: CVPR (2021)
Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: ICML (2015)
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: NeurIPS (2017)
Mishra, S., Saenko, K., Saligrama, V.: Surprisingly simple semi-supervised domain adaptation with pretraining and consistency. In: BMVC (2021)
Mitsuzumi, Y., Irie, G., Ikami, D., Shibata, T.: Generalized domain adaptation. In: CVPR (2021)
Na, J., Jung, H., Chang, H.J., Hwang, W.: FixBi: bridging domain spaces for unsupervised domain adaptation. In: CVPR (2021)
Nguyen, V.A., Nguyen, T., Le, T., Tran, Q.H., Phung, D.: STEM: an approach to multi-source domain adaptation with guarantees. In: ICCV (2021)
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69–84. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_5
Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.V.: Cats and dogs. In: CVPR (2012)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: CVPR (2016)
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: ICCV (2019)
Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., Saenko, K.: VisDA: the visual domain adaptation challenge. arXiv preprint arXiv:1710.06924 (2017)
Prabhu, V., Khare, S., Kartik, D., Hoffman, J.: SENTRY: selective entropy optimization via committee consistency for unsupervised domain adaptation. In: ICCV (2021)
Qiu, Z., Zhang, Y., Lin, H., Niu, S., Liu, Y., Du, Q., Tan, M.: Source-free domain adaptation via avatar prototype generation and adaptation. In: IJCAI (2021)
Ren, Z., Lee, Y.J.: Cross-domain self-supervised multi-task feature learning using synthetic imagery. In: CVPR (2018)
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16
Saito, K., Kim, D., Sclaroff, S., Saenko, K.: Universal domain adaptation through self supervision. In: NeurIPS (2020)
Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 156–171. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01228-1_10
Salman, H., Ilyas, A., Engstrom, L., Kapoor, A., Madry, A.: Do adversarially robust ImageNet models transfer better? In: NeurIPS (2020)
Scalbert, M., Vakalopoulou, M., Couzini’e-Devy, F.: Multi-source domain adaptation via supervised contrastive learning and confident consistency regularization. In: BMVC (2021)
Sivaprasad, P.T., Fleuret, F.: Uncertainty reduction for model adaptation in semantic segmentation. In: CVPR (2021)
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
Sun, Y., Tzeng, E., Darrell, T., Efros, A.A.: Unsupervised domain adaptation through self-supervision. arXiv preprint arXiv:1909.11825 (2019)
Tang, H., Chen, K., Jia, K.: Unsupervised domain adaptation via structurally regularized deep clustering. In: CVPR (2020)
Venkat, N., Kundu, J.N., Singh, D.K., Revanur, A., Babu, R.V.: Your classifier can secretly suffice multi-source domain adaptation. In: NeurIPS (2020)
Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: CVPR (2017)
Wallace, B., Hariharan, B.: Extending and analyzing self-supervised learning across domains. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 717–734. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58574-7_43
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.: ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: CVPR (2017)
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: CVPR (2018)
Xia, H., Zhao, H., Ding, Z.: Adaptive adversarial network for source-free domain adaptation. In: ICCV (2021)
Yang, S., Wang, Y., van de Weijer, J., Herranz, L., Jui, S.: Exploiting the intrinsic neighborhood structure for source-free domain adaptation. In: NeurIPS (2021)
Yang, S., Wang, Y., van de Weijer, J., Herranz, L., Jui, S.: Generalized source-free domain adaptation. In: ICCV (2021)
You, K., Long, M., Cao, Z., Wang, J., Jordan, M.I.: Universal domain adaptation. In: CVPR (2019)
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649–666. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_40
Zhang, R., Isola, P., Efros, A.A.: Split-brain autoencoders: unsupervised learning by cross-channel prediction. In: CVPR (2017)
Zhao, H., Combes, R.T.D., Zhang, K., Gordon, G.: On learning invariant representations for domain adaptation. In: ICML (2019)
Acknowledgments
This work was supported by MeitY (Ministry of Electronics and Information Technology) project (No. 4(16)2019-ITEA), Govt. of India and a research grant by Google.
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Kundu, J.N., Bhambri, S., Kulkarni, A., Sarkar, H., Jampani, V., Babu, R.V. (2022). Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13690. Springer, Cham. https://doi.org/10.1007/978-3-031-20056-4_11
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