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arXiv · 2007.09343

MTL2L: A Context Aware Neural Optimiser

Nicholas I-Hsien Kuo·Mehrtash Harandi·Nicolas Fourrier·Christian Walder·Gabriela Ferraro·Hanna Suominen
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Abstract

Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner. Previously, it was shown that a meta-learner could learn the direct rules to update learner parameters; and that the learnt neural optimiser updated learners more rapidly than handcrafted gradient-descent methods. However, we demonstrate that previous neural optimisers were limited to update learners on one designated dataset. In order to address input-domain heterogeneity, we introduce Multi-Task Learning to Learn (MTL2L), a context aware neural optimiser which self-modifies its optimisation rules based on input data. We show that MTL2L is capable of updating learners to classify on data of an unseen input-domain at the meta-testing phase.

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Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen. 2020-07-18. MTL2L: A Context Aware Neural Optimiser. https://arxiv.org/abs/2007.09343

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