SymCL: Riemannian Contrastive Learning on the Symmetric Positive Definite Manifold for Visual Classification
Keywords: Learning with Manifolds, Self-Supervised Learning, Classification
TL;DR: Contrastive Learning for Visual Classification Based on Riemannian Manifolds
Abstract: Symmetric Positive Definite (SPD) matric has been proven to be an effective feature descriptor in the realm of artificial intelligence, as it can encode spatiotemporal statistical information of data on a curved Riemannian manifold, \textit{i.e.}, SPD manifold. Although existing Riemannian neural networks have demonstrated superiority in many scientific fields, the inherent reliance on labels within supervised learning renders them susceptible to label errors. Besides, it is insufficient to depend solely on labels to learn effective feature distributions in some complicated data scenarios. Drawing inspiration from the considerable achievements of contrastive learning (CL) across diverse tasks, we extend the conventional CL paradigm to the context of SPD manifolds, which we denote SymCL, paving the way for a novel approach in SPD matrix-based visual classification. Furthermore, we inject a Riemannian triplet loss-based Riemannian metric learning (RML) into the designed SPD manifold CL framework for the sake of improving the discrimination of the learned geometric representations. Extensive experimental results on four datasets verify the effectiveness of the proposed algorithm.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 1564
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