SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
It addresses data imbalance problems for machine learning practitioners, offering a practical solution that is incremental by building on generative models.
The paper tackles data imbalance in training data, which causes biased predictions and ethical issues, by proposing SYNAuG, a method that uses synthetic data to equalize distributions and achieves impressive performance, surpassing existing task-specific methods.
Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical. Inspired by recent developments in generative models, this paper explores the potential of synthetic data to address the data imbalance problem. To be specific, our method, dubbed SYNAuG, leverages synthetic data to equalize the unbalanced distribution of training data. Our experiments demonstrate that, although a domain gap between real and synthetic data exists, training with SYNAuG followed by fine-tuning with a few real samples allows to achieve impressive performance on diverse tasks with different data imbalance issues, surpassing existing task-specific methods for the same purpose.