  The problem of efficiently training and evaluating image classifiers that can
distinguish between a large number of object categories is considered. A novel
metric, sharpness, is proposed which is defined as the fraction of object
categories that are above a threshold accuracy. To estimate sharpness (along
with a confidence value), a technique called fraction-accurate estimation is
introduced which samples categories and samples instances from these
categories. In addition, a technique called parity partition coding, a special
type of error correcting output code, is introduced, increasing sharpness,
while reducing the multi-class problem to a multi-label one with exponentially
fewer outputs. We demonstrate that this approach outperforms the baseline model
for both MultiMNIST and CelebA, while requiring fewer parameters and exceeding
state of the art accuracy on individual labels.
