Task-Level Contrastiveness for Cross-Domain Few-Shot Learning
This addresses the issue of cross-domain few-shot learning for AI systems needing to adapt to new tasks with limited data, though it appears incremental as it builds on existing few-shot/meta-learning algorithms.
The paper tackles the problem of few-shot learning methods struggling to generalize across diverse domains by introducing task-level contrastiveness, which improves generalization and computational efficiency, achieving superior performance on the MetaDataset benchmark.
Few-shot classification and meta-learning methods typically struggle to generalize across diverse domains, as most approaches focus on a single dataset, failing to transfer knowledge across various seen and unseen domains. Existing solutions often suffer from low accuracy, high computational costs, and rely on restrictive assumptions. In this paper, we introduce the notion of task-level contrastiveness, a novel approach designed to address issues of existing methods. We start by introducing simple ways to define task augmentations, and thereafter define a task-level contrastive loss that encourages unsupervised clustering of task representations. Our method is lightweight and can be easily integrated within existing few-shot/meta-learning algorithms while providing significant benefits. Crucially, it leads to improved generalization and computational efficiency without requiring prior knowledge of task domains. We demonstrate the effectiveness of our approach through different experiments on the MetaDataset benchmark, where it achieves superior performance without additional complexity.