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Dynamic Curriculum Learning for Imbalanced Data Classification
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Human attribute analysis is a challenging task in the field of computer vision. One of the significant difficulties is brought from largely imbalance-distributed data. Conventional 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 adaptively adjust the sampling strategy and loss weight in each batch, which results in better ability of
doi:10.1109/iccv.2019.00512
dblp:conf/iccv/WangGYWY19
fatcat:kzthfmteorerdkbsg4h2ogtl24