SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917796324048896 |
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| author | Bouhsine, Taha Aaroussi, Imad El Faysal, Atik Huaxia, Wang |
| author_facet | Bouhsine, Taha Aaroussi, Imad El Faysal, Atik Huaxia, Wang |
| contents | We introduce a novel anchor-free contrastive learning (AFCL) method leveraging our proposed Similarity-Orthogonality (SimO) loss. Our approach minimizes a semi-metric discriminative loss function that simultaneously optimizes two key objectives: reducing the distance and orthogonality between embeddings of similar inputs while maximizing these metrics for dissimilar inputs, facilitating more fine-grained contrastive learning. The AFCL method, powered by SimO loss, creates a fiber bundle topological structure in the embedding space, forming class-specific, internally cohesive yet orthogonal neighborhoods. We validate the efficacy of our method on the CIFAR-10 dataset, providing visualizations that demonstrate the impact of SimO loss on the embedding space. Our results illustrate the formation of distinct, orthogonal class neighborhoods, showcasing the method's ability to create well-structured embeddings that balance class separation with intra-class variability. This work opens new avenues for understanding and leveraging the geometric properties of learned representations in various machine learning tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_05233 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning Bouhsine, Taha Aaroussi, Imad El Faysal, Atik Huaxia, Wang Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition We introduce a novel anchor-free contrastive learning (AFCL) method leveraging our proposed Similarity-Orthogonality (SimO) loss. Our approach minimizes a semi-metric discriminative loss function that simultaneously optimizes two key objectives: reducing the distance and orthogonality between embeddings of similar inputs while maximizing these metrics for dissimilar inputs, facilitating more fine-grained contrastive learning. The AFCL method, powered by SimO loss, creates a fiber bundle topological structure in the embedding space, forming class-specific, internally cohesive yet orthogonal neighborhoods. We validate the efficacy of our method on the CIFAR-10 dataset, providing visualizations that demonstrate the impact of SimO loss on the embedding space. Our results illustrate the formation of distinct, orthogonal class neighborhoods, showcasing the method's ability to create well-structured embeddings that balance class separation with intra-class variability. This work opens new avenues for understanding and leveraging the geometric properties of learned representations in various machine learning tasks. |
| title | SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.05233 |