AlirezaMorsali/ENRP

Ensemble Neural Representation Networks

24
/ 100
Experimental

This project helps researchers and engineers efficiently store and reconstruct various signals, like images, using a novel neural network approach. It takes raw signal data, such as an image, and outputs a highly compressed, continuous representation that can be quickly decoded with high fidelity. This tool is for machine learning practitioners and signal processing researchers working with implicit neural representations.

No commits in the last 6 months.

Use this if you need to compress and reconstruct signals with high accuracy, faster training times, and fewer computational resources than traditional implicit neural networks.

Not ideal if you are looking for a simple, off-the-shelf image or signal compression tool that doesn't involve machine learning model training.

signal-processing image-compression neural-networks data-representation computational-efficiency
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 11 / 25

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2

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License

Last pushed

Jan 05, 2022

Commits (30d)

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