Imbalanced Deep Learning by Minority Class Incremental Rectification

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 41; no. 6; pp. 1367 - 1381
• Main Authors: Dong, Qi, Gong, Shaogang, Zhu, Xiatian
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmark testing; Benchmarks; Class imbalanced deep learning; clothing attribute recognition; Computational modeling; Data mining; Data models; Deep learning; facial attribute recognition; hard sample mining; inter-class boundary rectification; Iterative methods; Machine learning; multi-label learning; person attribute recognition; Training; Training data

Model learning from class imbalanced training data is a long-standing and significant challenge for machine learning. In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from si...