Task-Specific Preconditioner for Cross-Domain Few-Shot Learning
This addresses the problem of inefficient adaptation in few-shot learning across varying domains, offering a novel optimization method that improves performance, though it is incremental as it builds on existing parameterization approaches.
The paper tackles the sub-optimality of fixed optimization strategies in Cross-Domain Few-Shot Learning by proposing Task-Specific Preconditioned gradient descent, which meta-learns domain-specific preconditioners and combines them adaptively, achieving state-of-the-art performance on the Meta-Dataset.
Cross-Domain Few-Shot Learning~(CDFSL) methods typically parameterize models with task-agnostic and task-specific parameters. To adapt task-specific parameters, recent approaches have utilized fixed optimization strategies, despite their potential sub-optimality across varying domains or target tasks. To address this issue, we propose a novel adaptation mechanism called Task-Specific Preconditioned gradient descent~(TSP). Our method first meta-learns Domain-Specific Preconditioners~(DSPs) that capture the characteristics of each meta-training domain, which are then linearly combined using task-coefficients to form the Task-Specific Preconditioner. The preconditioner is applied to gradient descent, making the optimization adaptive to the target task. We constrain our preconditioners to be positive definite, guiding the preconditioned gradient toward the direction of steepest descent. Empirical evaluations on the Meta-Dataset show that TSP achieves state-of-the-art performance across diverse experimental scenarios.