Learning to Learn with Contrastive Meta-Objective

• URL: http://arxiv.org/abs/2410.05975v5
• Date: Fri, 07 Nov 2025 11:05:12 GMT
• Title: Learning to Learn with Contrastive Meta-Objective
• Authors: Shiguang Wu, Yaqing Wang, Yatao Bian, Quanming Yao,
• Abstract summary: We propose to exploit task identity as additional supervision in meta-training.<n>The proposed ConML is evaluating and optimizing the contrastive meta-objective.<n>We demonstrate that ConML integrates seamlessly with existing meta-learners, as well as in-context learning models.
• Score: 48.27877062976768
• License: http://creativecommons.org/licenses/by/4.0/
• 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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