Title: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor. Abstract: Recent findings indicate that over-parametrization, while crucial to the success of deep learning, also introduces large amounts of redundancy. Tensor methods have the potential to parametrize over-complete representations in a compact manner by leveraging this redundancy. In this paper, we propose fully parametrizing Convolutional Neural Networks (CNNs) with a single, low-rank tensor. Previous works on network tensorization haved focused on parametrizing individual layers (convolutional or fully connected) only, and perform the tensorization layer-by-layer disjointly. In contrast, we propose to jointly capture the full structure of a CNN by parametrizing it with a single, high-order tensor, the modes of which represent each of the architectural design parameters of the CNN (e.g. number of convolutional blocks, depth, number of stacks, input features, etc). This parametrization allows to regularize the whole network and drastically reduce the number of parameters by imposing a low-rank structure on that tensor. Further, our network is end-to-end trainable from scratch, which has been shown to be challenging in prior work. We study the case of networks with rich structure, namely Fully Convolutional CNNs, which we propose to parametrize them with a single 8-dimensional tensor. We show that our approach can achieve superior performance with small compression rates, and attain high compression rates with negligible drop in accuracy for the challenging task of human pose estimation.
