用组合互信息提升少样本学习的数据检索多样性，效果更优
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation
- 基于组合互信息设计新检索策略，兼顾相似性与多样性
- 在LAION-2B数据集上检索，显著提升图像分类准确率
- 计算开销极低，适合实际部署的少样本场景
检索增强通过从大型辅助数据池中获取额外数据，已成为低数据环境下提升模型性能的有效方法。以往方法仅采用最近邻策略进行数据选择，虽能选取与目标任务样本高度相似的辅助样本，但易导致样本冗余，缺乏多样性考量。本文首次证明，先前检索增强少样本适配中的数据选择策略可统一为一类称为组合互信息（CMI）的函数形式。为此提出COBRA（COmBinatorial Retrieval Augmentation），采用一种同时考虑多样性与目标数据集相似性的替代性CMI度量。在图像分类任务及多种少样本学习技术中，使用LAION-2B数据集检索时，COBRA始终优于现有方法。该方法引入的计算开销可忽略不计，却显著提升了下游模型性能。
原文摘要 · Abstract (English)
Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data regime. Prior approaches have employed only nearest-neighbor based strategies for data selection, which retrieve auxiliary samples with high similarity to instances in the target task. However, these approaches are prone to selecting highly redundant samples, since they fail to incorporate any notion of diversity. In our work, we first demonstrate that data selection strategies used in prior retrieval-augmented few-shot adaptation settings can be generalized using a class of functions known as Combinatorial Mutual Information (CMI) measures. We then propose COBRA (COmBinatorial Retrieval Augmentation), which employs an alternative CMI measure that considers both diversity and similarity to a target dataset. COBRA consistently outperforms previous retrieval approaches across image classification tasks and few-shot learning techniques when used to retrieve samples from LAION-2B. COBRA introduces negligible computational overhead to the cost of retrieval while providing significant gains in downstream model performance.
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