Ensemble Neural Representation Networks

TL;DR

This paper introduces an ensemble neural network architecture for implicit neural representations that reduces training time and computational resources while improving signal reconstruction quality.

Contribution

It proposes a novel ensemble structure for INR, along with an optimization algorithm to find sub-optimal configurations, enhancing efficiency and performance.

Findings

Fewer FLOPs and less training time compared to existing methods.

Improved PSNR performance in signal reconstruction.

Ensemble approach effectively divides tasks for better efficiency.

Abstract

Implicit Neural Representation (INR) has recently attracted considerable attention for storing various types of signals in continuous forms. The existing INR networks require lengthy training processes and high-performance computational resources. In this paper, we propose a novel sub-optimal ensemble architecture for INR that resolves the aforementioned problems. In this architecture, the representation task is divided into several sub-tasks done by independent sub-networks. We show that the performance of the proposed ensemble INR architecture may decrease if the dimensions of sub-networks increase. Hence, it is vital to suggest an optimization algorithm to find the sub-optimal structure of the ensemble network, which is done in this paper. According to the simulation results, the proposed architecture not only has significantly fewer floating-point operations (FLOPs) and less training time, but it also has better performance in terms of Peak Signal to Noise Ratio (PSNR) compared to those of its counterparts.