Summary of Simo Loss: Anchor-free Contrastive Loss For Fine-grained Supervised Contrastive Learning, by Taha Bouhsine et al.
SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning
by Taha Bouhsine, Imad El Aaroussi, Atik Faysal, Wang Huaxia
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed anchor-free contrastive learning (AFCL) method leverages a novel Similarity-Orthogonality (SimO) loss, which minimizes a semi-metric discriminative loss function that optimizes both reducing the distance and orthogonality between embeddings of similar inputs while maximizing these metrics for dissimilar inputs. This approach facilitates more fine-grained contrastive learning by creating class-specific, internally cohesive yet orthogonal neighborhoods in the embedding space. The method is validated on the CIFAR-10 dataset, demonstrating the formation of distinct, orthogonal class neighborhoods that balance class separation with intra-class variability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AFCL is a new way to learn representations without needing anchors. It uses a special loss called SimO to create a map in the representation space that groups similar things together while keeping them separate from other groups. This helps make better contrastive learning models by letting them understand what makes things similar or different. The method was tested on pictures of animals and vehicles, showing it can find distinct neighborhoods for each class. |
Keywords
» Artificial intelligence » Embedding space » Loss function
