Summary of Taxes Are All You Need: Integration Of Taxonomical Hierarchy Relationships Into the Contrastive Loss, by Kiran Kokilepersaud et al.
Taxes Are All You Need: Integration of Taxonomical Hierarchy Relationships into the Contrastive Loss
by Kiran Kokilepersaud, Yavuz Yarici, Mohit Prabhushankar, Ghassan AlRegib
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel supervised contrastive loss that incorporates taxonomic hierarchy information during representation learning. The approach directly penalizes the structure of the representation space, enabling greater flexibility in encoding semantic concepts. While traditional supervised contrastive losses only enforce semantic structure based on class labels, this method explicitly accounts for higher-order relationships within taxonomies, such as all animals with wings being “birds”. By integrating the loss into various settings, including medical and noise-based scenarios, the paper shows performance improvements of up to 7%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers understand pictures. Right now, computers are good at recognizing things like cats or dogs, but they struggle to recognize relationships between different categories of things. For example, all animals with wings being “birds”. The researchers developed a new method that takes these relationships into account and helps the computer learn more about what’s in a picture. This method works well even when the pictures are noisy or from a medical context. |
Keywords
» Artificial intelligence » Contrastive loss » Representation learning » Supervised
