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Avoidance of collapse is empirical and configuration dependent, and downstream gains are bounded to reported evaluations.", "domain": "Self-supervised image representation learning" }, "sources": [ { "citation": "Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning", "url": "https://arxiv.org/abs/2006.07733v1" }, { "citation": "Grill et al.: BYOL online-target self-supervised visual learning — author/institution implementation", "url": "https://github.com/google-deepmind/deepmind-research/tree/master/byol" } ], "statement": "# Grill et al.: BYOL online-target self-supervised visual learning\n\nTrained an online encoder/predictor to match another augmented view encoded by a slowly moving target network, obtaining visual representations without explicit negative pairs.\n\n## Scientific scope\n\nMomentum targets and Siamese learning have predecessors; contribution is this bootstrap recipe, not their original invention. Avoidance of collapse is empirical and configuration dependent, and downstream gains are bounded to reported evaluations.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nJean-Bastien Grill; Florian Strub; Florent Altché; Corentin Tallec; Pierre H. Richemond; Elena Buchatskaya; Carl Doersch; Bernardo Avila Pires; Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Bilal Piot; Koray Kavukcuoglu; Rémi Munos; Michal Valko\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning](https://arxiv.org/abs/2006.07733v1)\n- [Grill et al.: BYOL online-target self-supervised visual learning — author/institution implementation](https://github.com/google-deepmind/deepmind-research/tree/master/byol)\n", "summary": "Trained an online encoder/predictor to match another augmented view encoded by a slowly moving target network, obtaining visual representations without explicit negative pairs.", "title": "Grill et al.: BYOL online-target self-supervised visual learning" }, "record_id": "urn:uuid:0ecddf44-addd-4a4c-a610-61862296d20c", "version": 1, "source_ref": { "record_id": "urn:uuid:0ecddf44-addd-4a4c-a610-61862296d20c", "version": 1, "content_hash": "sha256:1f3f8a98056a382111745c04927cce41b2de1a7fc37f8c5b2efcb54b6dcd9883" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:99539c1c0144233169b90656fc7c89d63ce32c6d0ef5b61fe0a786daf517b505" }, "target": { "record_id": "urn:uuid:0ecddf44-addd-4a4c-a610-61862296d20c", "version": 1, "content_hash": "sha256:1f3f8a98056a382111745c04927cce41b2de1a7fc37f8c5b2efcb54b6dcd9883" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "grill-et-al-byol-selfsupervision-2020", "entry_id": "grill-et-al-byol-selfsupervision-2020", "revision": 1, "title": "Grill et al.: BYOL online-target self-supervised visual learning", "context": "Self-supervised image representation learning", "track": "Human-designed research method or resource", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Trained an online encoder/predictor to match another augmented view encoded by a slowly moving target network, obtaining visual representations without explicit negative pairs.", "scope": "Trained an online encoder/predictor to match another augmented view encoded by a slowly moving target network, obtaining visual representations without explicit negative pairs. Momentum targets and Siamese learning have predecessors; contribution is this bootstrap recipe, not their original invention. Avoidance of collapse is empirical and configuration dependent, and downstream gains are bounded to reported evaluations.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Momentum targets and Siamese learning have predecessors; contribution is this bootstrap recipe, not their original invention. 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