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Massive classification, a classification task defined over a vast number ofclasses (hundreds of thousands or even millions), has become an essential partof 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 wouldmeet with substantial difficulties, e.g. excessive memory demand andcomputational cost, when applied to massive problems. We present a new methodto tackle this problem. This method can efficiently and accurately identify asmall number of "active classes" for each mini-batch, based on a set of dynamicclass hierarchies constructed on the fly. We also develop an adaptiveallocation scheme thereon, which leads to a better tradeoff between performanceand cost. On several large-scale benchmarks, our method significantly reducesthe training cost and memory demand, while maintaining competitive performance.

Accelerated Training for Massive Classification via Dynamic Class Selection