提出任务级对比性方法，提升跨域小样本学习的泛化能力。
Task-Level Contrastiveness for Cross-Domain Few-Shot Learning
- 通过任务增强与对比损失，实现无监督任务表征聚类。
- 在MetaDataset上显著提升跨域性能，计算开销低。
- 可无缝集成到现有算法，无需领域先验知识。
小样本分类与元学习方法通常难以在不同领域间泛化，因多数方法仅针对单一数据集，无法在多种已见和未见领域间迁移知识。现有方案常面临准确率低、计算成本高及假设受限的问题。本文提出任务级对比性概念，引入简单的任务增强方式，并设计任务级对比损失，以促进任务表征的无监督聚类。该方法轻量且易于集成至现有小样本/元学习算法，显著提升泛化能力与计算效率，无需任务领域先验知识。我们在MetaDataset基准上通过多组实验验证其有效性，性能优于现有方法，且不增加额外复杂度。
原文摘要 · Abstract (English)
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.
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