Mining Minority-class Examples With Uncertainty Estimates
This addresses the challenge of mining tail-class examples in imbalanced datasets for computer vision applications, representing an incremental improvement over existing uncertainty-based approaches.
The paper tackles the problem of poor performance on rare classes in long-tail distributions by proposing a framework to mine minority-class examples, which substantially improves both mining effectiveness and fine-tuned model performance across three datasets in computer vision tasks.
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.