Influence-Balanced Loss for Imbalanced Visual Classification

In this paper, we propose a balancing training method to address problems in imbalanced data learning. To this end, we derive a new loss used in the balancing training phase that alleviates the influence of samples that cause an overfitted decision boundary. The proposed loss efficiently improves the performance of any type of imbalance learning methods. In experiments on multiple benchmark data sets, we demonstrate the validity of our method and reveal that the proposed loss outperforms the state-of-the-art cost-sensitive loss methods. Furthermore, since our loss is not restricted to a specific task, model, or training method, it can be easily used in combination with other recent resampling, meta-learning, and cost-sensitive learning methods for class-imbalance problems. Our code is made available at https://github.com/pseulki/IB-Loss.

1. Introduction

Despite the remarkable success of deep neural networks (DNNs) these days, many areas of computer vision suffer from highly imbalanced datasets. Many real-world data exhibit skewed distributions [23], [16], [11], [24], [10], in which the number of samples per class differs greatly. This imbalance between classes can be problematic, since the model trained on such imbalanced data tends to overfit the dominant (majority) classes [18], [14], [4]. That is, while the overall performance appears to be satisfactory, the model performs poorly on minority classes. To overcome the class imbalance problem, extensive research has recently been conducted to improve the generalization performance by reducing the overwhelming influence of the dominant class on the model.