Abstract
Official code for the NeurIPS 2023 paper "Neural Image Compression: Generalization, Robustness, and
Spectral Biases". This code can be used to:
- Computing spectral distortion errors of images compressed with traditional codes or neural image
compression models (as proposed in the aformentioned paper)
- Visualize power spectral densities and fourier heatmaps (as proposed in the aformentioned paper)
- Train and test a variety of neural image compression models
- Developers:
-
Lieberman, Kelsey [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2023-09-01
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 115924
- Site Accession Number:
- LLNL-CODE-856963
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Lieberman, Kelsey A.
Out of distribution evaluation for neural image compression.
Computer Software.
https://github.com/klieberman/ood_nic.
USDOE National Nuclear Security Administration (NNSA).
01 Sep. 2023.
Web.
doi:10.11578/dc.20231114.4.
Lieberman, Kelsey A.
(2023, September 01).
Out of distribution evaluation for neural image compression.
[Computer software].
https://github.com/klieberman/ood_nic.
https://doi.org/10.11578/dc.20231114.4.
Lieberman, Kelsey A.
"Out of distribution evaluation for neural image compression." Computer software.
September 01, 2023.
https://github.com/klieberman/ood_nic.
https://doi.org/10.11578/dc.20231114.4.
@misc{
doecode_115924,
title = {Out of distribution evaluation for neural image compression},
author = {Lieberman, Kelsey A.},
abstractNote = {Official code for the NeurIPS 2023 paper "Neural Image Compression: Generalization, Robustness, and
Spectral Biases". This code can be used to:
- Computing spectral distortion errors of images compressed with traditional codes or neural image
compression models (as proposed in the aformentioned paper)
- Visualize power spectral densities and fourier heatmaps (as proposed in the aformentioned paper)
- Train and test a variety of neural image compression models},
doi = {10.11578/dc.20231114.4},
url = {https://doi.org/10.11578/dc.20231114.4},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20231114.4}},
year = {2023},
month = {sep}
}