Summary of Galot: Generative Active Learning Via Optimizable Zero-shot Text-to-image Generation, by Hanbin Hong et al.
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation
by Hanbin Hong, Shenao Yan, Shuya Feng, Yan Yan, Yuan Hong
First submitted to arxiv on: 18 Dec 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 framework integrates zero-shot text-to-image synthesis and active learning to efficiently train machine learning models using text descriptions. The approach leverages active learning criteria to optimize text inputs for generating informative and diverse data samples, annotated by pseudo-labels crafted from text. This reduces the cost of data collection and annotation while increasing model training efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new framework that uses text-to-image synthesis and active learning to train machine learning models. The method optimizes text inputs to generate more information and diversity in data samples, which are then used for training. This approach helps reduce the cost of collecting and labeling data while improving model training efficiency. |
Keywords
» Artificial intelligence » Active learning » Image synthesis » Machine learning » Zero shot
