Summary of Foundation Model Makes Clustering a Better Initialization For Cold-start Active Learning, by Han Yuan and Chuan Hong
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning
by Han Yuan, Chuan Hong
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 method integrates foundation models with clustering methods to select samples for cold-start active learning initialization. Foundation models, trained on massive datasets using self-supervised learning, generate informative and compacted embeddings for various downstream tasks. These embeddings replace raw features like pixel values, allowing clustering to quickly converge and identify better initial samples. A classic ImageNet-supervised model is used as a baseline for comparison. Experiments on two clinical image classification and segmentation tasks demonstrate that foundation model-based clustering efficiently selects informative initial samples, leading to models with enhanced performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study proposes a new way of selecting samples for active learning when we don’t have any labeled data yet. It uses special kinds of AI models called “foundation models” to help group similar images together. These groups are then used as the starting point for training our machine learning model. The results show that this approach works better than just randomly picking images or using a simple grouping method. This is important because it can help us make better decisions when we have limited data. |
Keywords
* Artificial intelligence * Active learning * Clustering * Image classification * Machine learning * Self supervised * Supervised
