Summary of Parameter-efficient Active Learning For Foundational Models, by Athmanarayanan Lakshmi Narayanan et al.
Parameter-Efficient Active Learning for Foundational models
by Athmanarayanan Lakshmi Narayanan, Ranganath Krishnan, Amrutha Machireddy, Mahesh Subedar
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research proposes a novel approach to active learning (AL) by combining parameter-efficient fine-tuning methods with foundational vision transformer models. The study focuses on image datasets that exhibit out-of-distribution characteristics, making it particularly challenging. By evaluating the performance of this combination on these datasets, the authors demonstrate improved AL results and highlight the strategic advantage of merging these techniques. This contribution contributes to the broader discussion on optimizing AL strategies and presents a promising avenue for future exploration in leveraging foundation models for efficient and effective data annotation in specialized domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to use special AI models called foundational vision transformer models to improve the process of selecting which images to label when we only have a few examples. They try a new way of fine-tuning these models using less parameters, and then test it on some very difficult image datasets. The results show that this combination works really well and could be useful for annotating data in specialized areas. |
Keywords
» Artificial intelligence » Active learning » Fine tuning » Parameter efficient » Vision transformer
