  Deep convolutional neural networks often perform poorly when faced with
datasets that suffer from quantity imbalances and classification difficulties.
Despite advances in the field, existing two-stage approaches still exhibit
dataset bias or domain shift. To counter this, a phased progressive learning
schedule has been proposed that gradually shifts the emphasis from
representation learning to training the upper classifier. This approach is
particularly beneficial for datasets with larger imbalances or fewer samples.
Another new method a coupling-regulation-imbalance loss function is proposed,
which combines three parts: a correction term, Focal loss, and LDAM loss. This
loss is effective in addressing quantity imbalances and outliers, while
regulating the focus of attention on samples with varying classification
difficulties. These approaches have yielded satisfactory results on several
benchmark datasets, including Imbalanced CIFAR10, Imbalanced CIFAR100,
ImageNet-LT, and iNaturalist 2018, and can be easily generalized to other
imbalanced classification models.
