Summary of Cobra: Combinatorial Retrieval Augmentation For Few-shot Learning, by Arnav M. Das et al.
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Learning
by Arnav M. Das, Gantavya Bhatt, Lilly Kumari, Sahil Verma, Jeff Bilmes
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Retrieval augmentation, a technique for enhancing model performance in low-data regimes, has been shown to be effective in few-shot learning settings. Prior approaches relied on nearest-neighbor based strategies, which select auxiliary samples with high similarity to target task instances. However, these methods are prone to selecting highly redundant samples and fail to incorporate diversity considerations. Our work proposes COBRA (Combinatorial Retrieval Augmentation), a novel approach that employs Combinatorial Mutual Information (CMI) measures to balance diversity and similarity in data selection. COBRA outperforms previous retrieval approaches across image classification tasks and few-shot learning techniques, with negligible computational overhead and significant gains in downstream model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a hard time finding the right information because you don’t have much data to work with. This is called the “low-data regime.” To help with this problem, researchers developed a technique called “retrieval augmentation.” It helps models learn better by bringing in more relevant information from other places. The old way of doing this was by finding things that are very similar to what you’re looking for. But this method had some problems, like picking too many of the same things and not getting enough variety. Our new approach, called COBRA, does a better job of balancing similarity and diversity in its search. This helps models learn even more effectively, with only a small increase in computing time. |
Keywords
» Artificial intelligence » Few shot » Image classification » Nearest neighbor
