Ensemble Neural Representation Networks

• URL: http://arxiv.org/abs/2110.04124v1
• Date: Thu, 7 Oct 2021 12:49:21 GMT
• Title: Ensemble Neural Representation Networks
• Authors: Milad Soltany Kadarvish, Hesam Mojtahedi, Hossein Entezari Zarch, Amirhossein Kazerouni, Alireza Morsali, Azra Abtahi, Farokh Marvasti
• Abstract summary: Implicit Neural Representation (INR) has attracted considerable attention for storing various types of signals in continuous forms. We propose a novel sub-optimal ensemble architecture for INR that resolves the aforementioned problems. We show that the performance of the proposed ensemble INR architecture may decrease if the dimensions of sub-networks increase.
• Score: 10.405976966708744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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