Summary of Unsqueeze [cls] Bottleneck to Learn Rich Representations, by Qing Su et al.
Unsqueeze [CLS] Bottleneck to Learn Rich Representations
by Qing Su, Shihao Ji
First submitted to arxiv on: 24 Jul 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 paper introduces a new self-supervised learning approach called UDI, which aims to overcome the limitation of traditional distillation-based methods that tend to compress representations. UDI encourages multimodal prediction from local predictions derived via stratified sampling, resulting in more informative and semantically meaningful representations. The approach is evaluated on image classification, object detection, segmentation, and low-shot image classification tasks, showing superior or competitive results compared to state-of-the-art SSL methods. Additionally, UDI preserves the nuisance of input, leading to significant improvement in dense prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UDI helps computers learn better by making them think about different aspects of an image at the same time. This way, they can understand more about what’s happening in the image and make better predictions. The approach works well for tasks like recognizing objects, detecting people, and segmenting images into different parts. It even does well when there are only a few examples to learn from! |
Keywords
» Artificial intelligence » Distillation » Image classification » Object detection » Self supervised
