Ensemble Neural Representation Networks

Milad Soltany Kadarvish*, Hesam Mojtahedi*, Hossein Entezari Zarch*, Amirhossein Kazerouni*, Alireza Morsali,
Azra Abtahi, Farokh Marvasti

* Equal contribution

This is the official implementation of Ensemble Neural Representation Network in pytorch.

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.

Outputs

convergence.mp4

Results for 500 training steps.

Training

To run the program, you first need to clone the repo and install the requirements using the code below:

$ git clone https://github.com/AlirezaMorsali/ENRP.git
$ cd ENRP
$ pip install -r requirements.txt

To train the ENRP with your configuration, run the code below:

$ python single_model.py --input [your image] --grid[grid size] --depth[number of hidden layers] --width[number of hidden features] --n_steps 501 --batch_size 8

Grid size should be of two powers (1,2,4,8,16,32,...).

Citation

If you find our work useful in your research, please cite:

@article{kadarvish2021ensemble,
  title={Ensemble Neural Representation Networks},
  author={Kadarvish, Milad Soltany and Mojtahedi, Hesam and Zarch, Hossein Entezari and Kazerouni, Amirhossein and Morsali, Alireza and Abtahi, Azra and Marvasti, Farokh},
  journal={arXiv preprint arXiv:2110.04124},
  year={2021}
}