DisturbLabel: Regularizing CNN on the Loss Layer

CVPR 2016 Lingxi XieJingdong WangZhen WeiMeng WangQi Tian

During a long period of time we are combating over-fitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc. In this paper, we present DisturbLabel, an extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration... (read more)

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