Summary of Boundary Matters: a Bi-level Active Finetuning Framework, by Han Lu et al.
Boundary Matters: A Bi-Level Active Finetuning Framework
by Han Lu, Yichen Xie, Xiaokang Yang, Junchi Yan
First submitted to arxiv on: 15 Mar 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 The paper proposes a novel Bi-Level Active Finetuning framework to mitigate the high sample annotation costs in pretraining-finetuning tasks. This approach selects samples for annotation in one shot, using two stages: core sample selection for diversity and boundary sample selection for uncertainty. The process begins by identifying pseudo-class centers, followed by denoising and iterative boundary sample selection without relying on ground-truth labels. The framework outperforms existing baselines, offering a significant improvement in the active finetuning paradigm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make machine learning more efficient. It’s like when you’re trying to find the best questions for a teacher to answer. They came up with a new way to do this by choosing two types of samples: ones that are diverse and ones that are uncertain. This helps the model learn better without needing too many labeled examples. The method is called Bi-Level Active Finetuning, and it works really well. |
Keywords
* Artificial intelligence * Machine learning * One shot * Pretraining
