Summary of Reproducibility Study Of Cdul: Clip-driven Unsupervised Learning For Multi-label Image Classification, by Manan Shah et al.
Reproducibility Study of CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
by Manan Shah, Yash Bhalgat
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This reproducibility study verifies the effectiveness of CDUL, a CLIP-driven unsupervised learning method for multi-label image classification. The original paper introduced a novel aggregation strategy that initializes pseudo labels using the CLIP model and a gradient-alignment training method to update network parameters. Our contribution is a well-commented, open-sourced code implementation of the entire method, providing a reproducible framework for researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study makes sure that CDUL really works by testing its two main ideas: using the CLIP model to help with image classification, and adjusting the way we update our models as they learn. The authors share their code so others can use it too! |
Keywords
» Artificial intelligence » Alignment » Image classification » Unsupervised
