SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning
Keywords: Contrastive Learning, Anchor Free, Contrastive Learning, Explainable Contrastive Learning, Fine Grained Representation Learning
TL;DR: New anchor-free contrastive learning method using SimO loss optimizes distance and orthogonality between embeddings. Creates distinct, orthogonal class neighborhoods in embedding space.
Abstract: We introduce a novel anchor-free contrastive learning (AFCL) method leveraging our proposed Similarity-Orthogonality (SimO) loss. Our approach minimizes a pseudo-metric/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.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 12936
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