On Random Weights and Unsupervised Feature Learning

Abstract

Recently two anomalous results in the literature have shown that certain feature learning architectures can perform very well on object recognition tasks, without training. In this paper we pose the question, why do random weights sometimes do so well? Our answer is that certain convolutional pooling architectures can be inherently frequency selective and translation invariant, even with random weights. Based on this we demonstrate the viability of extremely fast architecture search by using random weights to evaluate candidate architectures, thereby sidestepping the time-consuming learning process. We then show that a surprising fraction of the performance of certain state-of-the-art methods can be attributed to the architecture alone. 1