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Dynamic Curriculum Learning for Imbalanced Data Classification
[article]
2019
arXiv
pre-print
Human attribute analysis is a challenging task in the field of computer vision, since the data is largely imbalance-distributed. Common techniques such as re-sampling and cost-sensitive learning require prior-knowledge to train the system. To address this problem, we propose a unified framework called Dynamic Curriculum Learning (DCL) to online adaptively adjust the sampling strategy and loss learning in single batch, which resulting in better generalization and discrimination. Inspired by the
arXiv:1901.06783v2
fatcat:7mdcol5jjvhtzdnzf5ls7fvwdm