SCoRe: Submodular Combinatorial Representation Learning for Real-World
  Class-Imbalanced Settings
Anay Majee Suraj Kothawade Krishnateja Killiamsetty Rishabh Iyer
Representation Learning in real-world class-imbalanced settings has emerged
as a challenging task in the evolution of deep learning. Lack of diversity in
visual and structural features for rare classes restricts modern neural
networks to learn discriminative feature clusters. This manifests in the form
of large inter-class bias between rare object classes and elevated intra-class
variance among abundant classes in the dataset. Although deep metric learning
approaches have shown promise in this domain, significant improvements need to
be made to overcome the challenges associated with class-imbalance in mission
critical tasks like autonomous navigation and medical diagnostics. Set-based
combinatorial functions like Submodular Information Measures exhibit properties
that allow them to simultaneously model diversity and cooperation among feature
clusters. In this paper, we introduce the SCoRe (Submodular Combinatorial
Representation Learning) framework and propose a family of Submodular
Combinatorial Loss functions to overcome these pitfalls in contrastive
learning. We also show that existing contrastive learning approaches are either
submodular or can be re-formulated to create their submodular counterparts. We
conduct experiments on the newly introduced family of combinatorial objectives
on two image classification benchmarks - pathologically imbalanced CIFAR-10,
subsets of MedMNIST and a real-world road object detection benchmark - India
Driving Dataset (IDD). Our experiments clearly show that the newly introduced
objectives like Facility Location, Graph-Cut and Log Determinant outperform
state-of-the-art metric learners by up to 7.6% for the imbalanced
classification tasks and up to 19.4% for object detection tasks.