Summary of Image Clustering Algorithm Based on Self-supervised Pretrained Models and Latent Feature Distribution Optimization, by Qiuyu Zhu and Liheng Hu and Sijin Wang
Image Clustering Algorithm Based on Self-Supervised Pretrained Models and Latent Feature Distribution Optimization
by Qiuyu Zhu, Liheng Hu, Sijin Wang
First submitted to arxiv on: 4 Aug 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 paper proposes a novel image clustering algorithm that leverages self-supervised pre-trained models and latent feature distribution optimization to enhance clustering performance. The authors demonstrate the effectiveness of their approach by fine-tuning pre-trained models on complex natural images, resulting in improved discriminative power of latent features. They also introduce a k-nearest neighbor-based method to further enhance discriminative power and optimize latent feature distributions. Experimental results on multiple datasets show that the proposed algorithm outperforms state-of-the-art clustering methods, achieving similar accuracy to supervised methods for small category sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to group images together based on their features. It uses pre-trained models and adjusts them to better recognize patterns in natural images. The authors test this approach on different datasets and show that it works well, even matching the results of more complex supervised approaches. This is an important step forward for image clustering. |
Keywords
» Artificial intelligence » Clustering » Fine tuning » Nearest neighbor » Optimization » Self supervised » Supervised
