Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification

Deep neural networks generally perform poorly with datasets that suffer from quantity imbalance and classification difficulty imbalance problems. Despite progress in this field, there still are problems of dataset bias or domain shift in the existing two-stage approaches. Therefore, a phased progressive learning schedule enabling smooth transfer of training emphasis from representation learning to upper classifier training is proposed. This has greater effectivity on datasets of severer imbalances or smaller scales. A coupling-regulation-imbalance loss function is designed, coupling a correction term, Focal loss, and LDAM loss. The loss can better deal with quantity imbalance and outliers while regulating the focus-of-attention of samples with different classification difficulties. These approaches achieved satisfactory results on multiple benchmark datasets, including Imbalanced CIFAR10, Imbalanced CIFAR100, ImageNet-LT, and iNaturalist 2018, and they can also be easily generalized for other imbalanced classification models.