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arXiv · 1801.01687

Accelerated Training for Massive Classification via Dynamic Class Selection

Xingcheng Zhang·Lei Yang·Junjie Yan·Dahua Lin
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Abstract

Massive classification, a classification task defined over a vast number of classes (hundreds of thousands or even millions), has become an essential part of many real-world systems, such as face recognition. Existing methods, including the deep networks that achieved remarkable success in recent years, were mostly devised for problems with a moderate number of classes. They would meet with substantial difficulties, e.g. excessive memory demand and computational cost, when applied to massive problems. We present a new method to tackle this problem. This method can efficiently and accurately identify a small number of "active classes" for each mini-batch, based on a set of dynamic class hierarchies constructed on the fly. We also develop an adaptive allocation scheme thereon, which leads to a better tradeoff between performance and cost. On several large-scale benchmarks, our method significantly reduces the training cost and memory demand, while maintaining competitive performance.

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Xingcheng Zhang, Lei Yang, Junjie Yan, Dahua Lin. 2018-01-05. Accelerated Training for Massive Classification via Dynamic Class Selection. https://arxiv.org/abs/1801.01687

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