  In this paper we introduce the SCoRe (Submodular Combinatorial Representation
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.
