  Real-world datasets are often highly class-imbalanced, which can adversely
impact the performance of deep learning models. The majority of research on
training neural networks under class imbalance has focused on specialized loss
functions, sampling techniques, or two-stage training procedures. Notably, we
demonstrate that simply tuning existing components of standard deep learning
pipelines, such as the batch size, data augmentation, optimizer, and label
smoothing, can achieve state-of-the-art performance without any such
specialized class imbalance methods. We also provide key prescriptions and
considerations for training under class imbalance, and an understanding of why
imbalance methods succeed or fail.
