Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification. (arXiv:2205.12117v2 [cs.LG] UPDATED)

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 …

arxiv classification data data classification loss regulation