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

• URL: http://arxiv.org/abs/2205.12117v1
• Date: Tue, 24 May 2022 14:46:39 GMT
• Title: Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Classification
• Authors: Liang Xu, Yi Cheng, Fan Zhang, Bingxuan Wu, Pengfei Shao, Peng Liu, Shuwei Shen, Peng Yao, Ronald X.Xu
• Abstract summary: Deep neural networks generally perform poorly with datasets that suffer from quantity imbalance and classification difficulty imbalance between different classes. A phased progressive learning schedule was proposed for smoothly transferring the training emphasis from representation learning to upper classifier training. Our code will be open source soon.
• Score: 11.673344551762822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks generally perform poorly with datasets that suffer from quantity imbalance and classification difficulty imbalance between different classes. In order to alleviate the problem of dataset bias or domain shift in the existing two-stage approaches, a phased progressive learning schedule was proposed for smoothly transferring the training emphasis from representation learning to upper classifier training. This has greater effectivity on datasets that have more severe imbalances or smaller scales. A coupling-regulation-imbalance loss function was designed, coupling a correction term, Focal loss and LDAM loss. Coupling-regulation-imbalance loss can better deal with quantity imbalance and outliers, while regulating focus-of-attention of samples with a variety of classification difficulties. Excellent results were achieved on multiple benchmark datasets using these approaches and they can be easily generalized for other imbalanced classification models. Our code will be open source soon.

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