The effects of negative adaptation in Model-Agnostic Meta-Learning
The capacity of meta-learning algorithms to quickly adapt to a variety of tasks, including ones they did not experience during meta-training, has been a key factor in the recent success of these methods on few-shot learning problems. This particular advantage of using meta-learning over standard supervised or reinforcement learning is only well founded under the assumption that the adaptation phase does improve the performance of our model on the task of interest. However, in the classical framework of meta-learning, this constraint is only mildly enforced, if not at all, and we only see an improvement on average over a distribution of tasks. In this paper, we show that the adaptation in an algorithm like MAML can significantly decrease the performance of an agent in a meta-reinforcement learning setting, even on a range of meta-training tasks.
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Tristan Deleu (edit)
Yoshua Bengio (edit)
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12/05/18 06:01PM
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reddit_ml: [R] The effects of negative adaptation in Model-Agnostic Meta-Learning: the adaptation in an algorithm like MAML ... https://t.co/cEJRb6goiq
TristanDeleu: I am presenting our work on the effects of negative adaptation in MAML at the Meta-Learning workshop this Saturday (Room 220 E) at #NeurIPS2018 https://t.co/Prw0Uk0jxJ https://t.co/LilNPxeTYq
StatsPapers: The effects of negative adaptation in Model-Agnostic Meta-Learning. https://t.co/o4uCzRTdFi
arxivml: "The effects of negative adaptation in Model-Agnostic Meta-Learning", Tristan Deleu, Yoshua Bengio https://t.co/t4jgIZtoVg
BrundageBot: The effects of negative adaptation in Model-Agnostic Meta-Learning. Tristan Deleu and Yoshua Bengio https://t.co/fS66WLh26X
M157q_News_RSS: The effects of negative adaptation in Model-Agnostic Meta-Learning. (arXiv:1812.02159v1 [cs.LG]) https://t.co/YaYESPZLyv The capacity of meta-learning algorithms to quickly adapt to a variety of tasks, including ones they did not experience during meta-training, has been a key fa
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