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arXiv · 2410.05975

Learning to Learn with Contrastive Meta-Objective

Shiguang Wu·Yaqing Wang·Yatao Bian·Quanming Yao
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Abstract

Meta-learning enables learning systems to adapt quickly to new tasks, similar to humans. Different meta-learning approaches all work under/with the mini-batch episodic training framework. Such framework naturally gives the information about task identity, which can serve as additional supervision for meta-training to improve generalizability. We propose to exploit task identity as additional supervision in meta-training, inspired by the alignment and discrimination ability which is is intrinsic in human's fast learning. This is achieved by contrasting what meta-learners learn, i.e., model representations. The proposed ConML is evaluating and optimizing the contrastive meta-objective under a problem- and learner-agnostic meta-training framework. We demonstrate that ConML integrates seamlessly with existing meta-learners, as well as in-context learning models, and brings significant boost in performance with small implementation cost.

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Shiguang Wu, Yaqing Wang, Yatao Bian, Quanming Yao. 2024-10-08. Learning to Learn with Contrastive Meta-Objective. https://arxiv.org/abs/2410.05975

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