Mining Minority-class Examples With Uncertainty Estimates

TL;DR

This paper introduces a novel method for mining minority-class examples in long-tail distributions by enhancing tail-class activations and using a one-class approach, leading to improved model performance.

Contribution

It presents a simple, effective framework that overcomes challenges in uncertainty-based tail-class mining caused by data skewness.

Findings

Significant improvement in minority-class mining accuracy

Enhanced model performance on long-tail datasets

Robust evaluation across multiple datasets and tasks

Abstract

In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare classes. A promising solution is to mine tail-class examples to balance the training dataset. However, mining tail-class examples is a very challenging task. For instance, most of the otherwise successful uncertainty-based mining approaches struggle due to distortion of class probabilities resulting from skewness in data. In this work, we propose an effective, yet simple, approach to overcome these challenges. Our framework enhances the subdued tail-class activations and, thereafter, uses a one-class data-centric approach to effectively identify tail-class examples. We carry out an exhaustive evaluation of our framework on three datasets spanning over two computer vision tasks. Substantial improvements in the minority-class mining and fine-tuned model's performance strongly corroborate the value of our proposed solution.