{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/meta-learning-with-latent-embedding","title":"Meta-Learning with Latent Embedding Optimization","arxiv_id":"1807.05960","date":"2018-07-16","proceeding":"ICLR 2019 5","authors":["Andrei A. Rusu","Dushyant Rao","Jakub Sygnowski","Oriol Vinyals","Razvan Pascanu","Simon Osindero","Raia Hadsell"],"abstract":"Gradient-based meta-learning techniques are both widely applicable and\nproficient at solving challenging few-shot learning and fast adaptation\nproblems. However, they have practical difficulties when operating on\nhigh-dimensional parameter spaces in extreme low-data regimes. We show that it\nis possible to bypass these limitations by learning a data-dependent latent\ngenerative representation of model parameters, and performing gradient-based\nmeta-learning in this low-dimensional latent space. The resulting approach,\nlatent embedding optimization (LEO), decouples the gradient-based adaptation\nprocedure from the underlying high-dimensional space of model parameters. Our\nevaluation shows that LEO can achieve state-of-the-art performance on the\ncompetitive miniImageNet and tieredImageNet few-shot classification tasks.\nFurther analysis indicates LEO is able to capture uncertainty in the data, and\ncan perform adaptation more effectively by optimizing in latent space.","url_abs":"http://arxiv.org/abs/1807.05960v3","url_pdf":"http://arxiv.org/pdf/1807.05960v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"meta-learning-with-latent-embedding","repo_url":"https://github.com/deepmind/leo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"meta-learning-with-latent-embedding","repo_url":"https://github.com/xiangyu8/PT-MAP-sf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"meta-learning-with-latent-embedding","repo_url":"https://github.com/yinboc/few-shot-meta-baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"meta-learning-with-latent-embedding","repo_url":"https://github.com/jiean001/models_m/tree/main/LEO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"meta-learning-with-latent-embedding","repo_url":"https://github.com/timchen0618/pytorch-leo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.05960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.05960"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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