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Burn After Reading: Online Adaptation for Cross-domain Streaming Data

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13693))

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Abstract

In the context of online privacy, many methods propose complex security preserving measures to protect sensitive data. In this paper we note that: not storing any sensitive data is the best form of security. We propose an online framework called “Burn After Reading”, i.e. each online sample is permanently deleted after it is processed. Our framework utilizes the labels from the public data and predicts on the unlabeled sensitive private data. To tackle the inevitable distribution shift from the public data to the private data, we propose a novel unsupervised domain adaptation algorithm that aims at the fundamental challenge of this online setting–the lack of diverse source-target data pairs. We design a Cross-Domain Bootstrapping approach, named CroDoBo, to increase the combined data diversity across domains. To fully exploit the valuable discrepancies among the diverse combinations, we employ the training strategy of multiple learners with co-supervision. CroDoBo achieves state-of-the-art online performance on four domain adaptation benchmarks. Code is available here.

C. Ramaiah—Work was done at Salesforce.

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Notes

  1. 1.

    Article 17 GDPR - Right to be forgotten https://gdpr.eu/article-17-right-to-be-forgotten/.

  2. 2.

    https://wilds.stanford.edu/leaderboard/.

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Authors and Affiliations

  1. University of Maryland, College Park, USA

    Luyu Yang & Abhinav Shrivastava

  2. Salesforce Research, Palo Alto, USA

    Mingfei Gao, Zeyuan Chen & Ran Xu

  3. Google, Menlo Park, USA

    Chetan Ramaiah

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  1. Luyu Yang
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  6. Chetan Ramaiah
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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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Yang, L., Gao, M., Chen, Z., Xu, R., Shrivastava, A., Ramaiah, C. (2022). Burn After Reading: Online Adaptation for Cross-domain Streaming Data. 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 13693. Springer, Cham. https://doi.org/10.1007/978-3-031-19827-4_24

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