Summary of Revisiting Active Learning in the Era Of Vision Foundation Models, by Sanket Rajan Gupte et al.
Revisiting Active Learning in the Era of Vision Foundation Models
by Sanket Rajan Gupte, Josiah Aklilu, Jeffrey J. Nirschl, Serena Yeung-Levy
First submitted to arxiv on: 25 Jan 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 Foundation vision or language models, trained on large datasets, can learn robust representations that excel in zero- and few-shot learning. These properties make them well-suited for active learning (AL), which aims to optimize labeling efficiency. This work explores how foundation models influence three critical AL components: initial labeled pool selection, ensuring diverse sampling, and the trade-off between representative and uncertainty sampling. We study how the robust representations of foundation models (DINOv2, OpenCLIP) challenge existing AL findings. Our observations inform a new simple and elegant AL strategy that balances uncertainty with sample diversity. We test our method on various image classification benchmarks, including natural images and understudied biomedical images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation vision or language models are special machines that can learn to recognize things even when they don’t have much data. These machines can be very good at guessing what something is without needing a lot of information. This makes them great for helping people label data more efficiently. In this study, researchers looked at how these machines affect three important parts of the labeling process: choosing which data to start with, making sure the selection is diverse, and deciding when to focus on finding new things or figuring out what’s already known. The results show that these machines can be used in a new way to make labeling more efficient. |
Keywords
* Artificial intelligence * Active learning * Few shot * Image classification
