Summary of Beclr: Batch Enhanced Contrastive Few-shot Learning, by Stylianos Poulakakis-daktylidis and Hadi Jamali-rad
BECLR: Batch Enhanced Contrastive Few-Shot Learning
by Stylianos Poulakakis-Daktylidis, Hadi Jamali-Rad
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed Dynamic Clustered mEmory (DyCE) module enhances the latent representation space in unsupervised contrastive learning, leading to improved positive sampling during pretraining. This module is part of a novel end-to-end approach called BECLR, which tackles sample bias at the few-shot inference stage using an Optimal Transport-based distribution Alignment (OpTA) strategy. Experimental results demonstrate that BECLR achieves state-of-the-art performance across various U-FSL benchmarks and outperforms current baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps machines learn quickly from just a few labeled samples, bridging the gap with humans. It does this by improving how computers represent data without needing lots of labels. The new approach combines two techniques: DyCE, which makes the computer’s “memory” better at grouping similar things together; and OpTA, which fixes problems caused by unequal sample sizes. By combining these two ideas, BECLR is a powerful tool that can be used for many different applications. |
Keywords
* Artificial intelligence * Alignment * Few shot * Inference * Pretraining * Unsupervised
