Class Rectification Hard Mining for Imbalanced Deep Learning

Qi Dong; Shaogang Gong; Xiatian Zhu

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arXiv · 1712.03162

Class Rectification Hard Mining for Imbalanced Deep Learning

Qi Dong·Shaogang Gong·Xiatian Zhu

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Abstract

Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes. To address this problem, we formulate a novel scheme for batch incremental hard sample mining of minority attribute classes from imbalanced large scale training data. We develop an end-to-end deep learning framework capable of avoiding the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes. This is made possible by introducing a Class Rectification Loss (CRL) regularising algorithm. We demonstrate the advantages and scalability of CRL over existing state-of-the-art attribute recognition and imbalanced data learning models on two large scale imbalanced benchmark datasets, the CelebA facial attribute dataset and the X-Domain clothing attribute dataset.

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BibTeX RIS

Qi Dong, Shaogang Gong, Xiatian Zhu. 2017-12-08. Class Rectification Hard Mining for Imbalanced Deep Learning. https://arxiv.org/abs/1712.03162

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