ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning

Shiguang Wu, Yaqing Wang, Yatao Bian, Quanming Yao

Keywords: Meta-Learning, Contrastive Learning, In-Context Learning

TL;DR: We propose to make meta-learners emulate the alignment and discrimination ability in human learning during meta-training, which result in a learner-agnostic approach that universally improves different meta-learners.

Abstract: Meta-learning enables learning systems to adapt quickly to new tasks, similar to humans. To emulate this human-like rapid learning and enhance alignment and discrimination abilities, we propose ConML, a universal meta-learning framework that can be applied to various meta-learning algorithms without relying on specific model architectures nor target models. The core of ConML is task-level contrastive learning, which extends contrastive learning from the representation space in unsupervised learning to the model space in meta-learning. By leveraging task identity as an additional supervision signal during meta-training, we contrast the outputs of the meta-learner in the model space, minimizing inner-task distance (between models trained on different subsets of the same task) and maximizing inter-task distance (between models from different tasks). We demonstrate that ConML integrates seamlessly with optimization-based, metric-based, and amortization-based meta-learning algorithms, as well as in-context learning, resulting in performance improvements across diverse few-shot learning tasks.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 13566