SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning

• URL: http://arxiv.org/abs/2410.05233v1
• Date: Mon, 7 Oct 2024 17:41:10 GMT
• Title: SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning
• Authors: Taha Bouhsine, Imad El Aaroussi, Atik Faysal, Wang Huaxia,
• Abstract summary: We introduce a novel anchor-free contrastive learning (L) method leveraging our proposed Similarity-Orthogonality (SimO) loss. Our approach minimizes a semi-metric discriminative loss function that simultaneously optimize two key objectives. We provide visualizations that demonstrate the impact of SimO loss on the embedding space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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