SCoRe: Submodular Combinatorial Representation Learning

In this paper we introduce the SCoRe ( S ubmodular Co mbinatorial Re presentation Learning) framework, a novel approach in representation learning that addresses inter-class bias and intra-class variance. SCoRe provides a new combinatorial viewpoint to representation learning, by introducing a family of loss functions based on set-based submodular information measures. We develop two novel combinatorial formulations for loss functions, using the Total Information and Total Correlation, that naturally minimize intra-class variance and inter-class bias. Several commonly used metric/contrastive learning loss functions like supervised contrastive loss, orthogonal projection loss, and N-pairs loss, are all instances of SCoRe, thereby underlining the versatility and applicability of SCoRe in a broad spectrum of learning scenarios. Novel objectives in SCoRe naturally model class-imbalance with up to 7. 6% improvement in classification on CIFAR-10-LT, CIFAR-100-LT, MedMNIST, 2. 1% on ImageNet-LT, and 19. 4% in object detection on IDD and LVIS (v1. 0), demonstrating its effectiveness over existing approaches.

Authors

• Anay Majee
• Suraj Kothawade
• Krishnateja Killamsetty
• Rishabh K. Iyer

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