Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror Descent

• URL: http://arxiv.org/abs/2312.13486v2
• Date: Thu, 18 Jan 2024 20:42:48 GMT
• Title: Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror Descent
• Authors: Yilang Zhang, Bingcong Li, Georgios B. Giannakis
• Abstract summary: A fundamental challenge in meta-learning is how to quickly "adapt" the extracted prior in order to train a task-specific model. Existing approaches deal with this challenge using a preconditioner that enhances convergence of the per-task training process. The present contribution addresses this limitation by learning a nonlinear mirror map, which induces a versatile distance metric.
• Score: 44.56938629818211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Utilizing task-invariant prior knowledge extracted from related tasks, meta-learning is a principled framework that empowers learning a new task especially when data records are limited. A fundamental challenge in meta-learning is how to quickly "adapt" the extracted prior in order to train a task-specific model within a few optimization steps. Existing approaches deal with this challenge using a preconditioner that enhances convergence of the per-task training process. Though effective in representing locally a quadratic training loss, these simple linear preconditioners can hardly capture complex loss geometries. The present contribution addresses this limitation by learning a nonlinear mirror map, which induces a versatile distance metric to enable capturing and optimizing a wide range of loss geometries, hence facilitating the per-task training. Numerical tests on few-shot learning datasets demonstrate the superior expressiveness and convergence of the advocated approach.

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