{"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/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","arxiv_id":"1703.03400","date":"2017-03-09","proceeding":"ICML 2017 8","authors":["Chelsea Finn","Pieter Abbeel","Sergey Levine"],"abstract":"We propose an algorithm for meta-learning that is model-agnostic, in the\nsense that it is compatible with any model trained with gradient descent and\napplicable to a variety of different learning problems, including\nclassification, regression, and reinforcement learning. The goal of\nmeta-learning is to train a model on a variety of learning tasks, such that it\ncan solve new learning tasks using only a small number of training samples. In\nour approach, the parameters of the model are explicitly trained such that a\nsmall number of gradient steps with a small amount of training data from a new\ntask will produce good generalization performance on that task. In effect, our\nmethod trains the model to be easy to fine-tune. We demonstrate that this\napproach leads to state-of-the-art performance on two few-shot image\nclassification benchmarks, produces good results on few-shot regression, and\naccelerates fine-tuning for policy gradient reinforcement learning with neural\nnetwork policies.","url_abs":"http://arxiv.org/abs/1703.03400v3","url_pdf":"http://arxiv.org/pdf/1703.03400v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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