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Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation

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Computer Vision – ECCV 2022 (ECCV 2022)

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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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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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Authors and Affiliations

  1. Indian Institute of Science, Bengaluru, India

    Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni, Hiran Sarkar & R. Venkatesh Babu

  2. Google Research, Cambridge, USA

    Varun Jampani

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  1. Jogendra Nath Kundu
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  2. Suvaansh Bhambri
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  3. Akshay Kulkarni
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  4. Hiran Sarkar
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  5. Varun Jampani
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  6. R. Venkatesh Babu
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Correspondence to Jogendra Nath Kundu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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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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  • DOI: https://doi.org/10.1007/978-3-031-20056-4_11

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