Universum Prescription: Regularization using Unlabeled Data

Xiang Zhang, Yann Lecun

2015-11-11Image Classification

Abstract

This paper shows that simply prescribing "none of the above" labels to unlabeled data has a beneficial regularization effect to supervised learning. We call it universum prescription by the fact that the prescribed labels cannot be one of the supervised labels. In spite of its simplicity, universum prescription obtained competitive results in training deep convolutional networks for CIFAR-10, CIFAR-100, STL-10 and ImageNet datasets. A qualitative justification of these approaches using Rademacher complexity is presented. The effect of a regularization parameter -- probability of sampling from unlabeled data -- is also studied empirically.

Results

Task	Dataset	Metric	Value	Model
Image Classification	CIFAR-10	Percentage correct	93.3	Universum Prescription
Image Classification	CIFAR-100	Percentage correct	67.2	Universum Prescription

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