Learning Differentiable Sparse and Low Rank Networks for Audio-Visual Object Localization

Abstract

Parsimonious modelling, including sparsity and low rankness, has becomes a cornerstone in modern machine learning and signal processing. However, these modelling techniques have limited capabity to learn from large-scale data, and often require some pre-defined parameters to define their optimization procedure. In this paper, we propose a novel method to design specific deep neural networks for sparse and low-rank models, where the network can learn a data-adaptive model from training data. In particular, we design differentiable network units for sparse and low-rank matrices. Each layer of the network represents one iteration of the optimization process of sparse and low-rank models. The effectiveness of the proposed method is evaluated in the task of audio-visual object localization. Experimental results indicate the superior performance of the proposed method over traditional sparse and low-rank models.