Exploiting Synthetic Data for Data Imbalance Problems: Baselines from a Data Perspective

We live in a vast ocean of data, and deep neural networks are no exception to this. However, this data exhibits an inherent phenomenon of imbalance. This imbalance poses a risk of deep neural networks producing biased predictions, leading to potentially severe ethical and social consequences. To address these challenges, we believe that the use of generative models is a promising approach for comprehending tasks, given the remarkable advancements demonstrated by recent diffusion models in generating high-quality images. In this work, we propose a simple yet effective baseline, SYNAuG, that utilizes synthetic data as a preliminary step before employing task-specific algorithms to address data imbalance problems. This straightforward approach yields impressive performance on datasets such as CIFAR100-LT, ImageNet100-LT, UTKFace, and Waterbird, surpassing the performance of existing task-specific methods. While we do not claim that our approach serves as a complete solution to the problem of data imbalance, we argue that supplementing the existing data with synthetic data proves to be an effective and crucial preliminary step in addressing data imbalance concerns.