In Search of a Data Transformation That Accelerates Neural Field Training

Paper | Hugging Face

Junwon Seo*, Sangyoon Lee*, Kwang In Kim, Jaeho Lee

Pohang University of Science and Technology (POSTECH)

This is the official Github page for the paper "In Search of a Data Transformation That Accelerates Neural Field Training" (CVPR 2024).

SIREN experiments

Our experiments on SIREN are based on the official code for the paper "Implicit Neural Representations with Periodic Activation Functions".

This repository does not contain any image datasets used in the manuscript.
We used Kodak, DIV2K, and CLIC datasets for our main experiments. (Section 3.1. in our paper for details)

Setup

To run a single SIREN experiment, execute the following command:

$ cd SIREN
### example code
$ python siren_DT.py --experiment_name=1 --lr=-10 --sidelength=512 --num_workers=16 --project=SIREN --max_steps=10000 --directory=kodak_ori --batch_size=18 --gpu_num=0 --type=origin

To run all experiments for the Kodak datset, use the following script:

$ cd SIREN
$ sh run_siren.sh

Descriptions of the command line flags are in /SIREN/README.md.

Loss Landscape

All loss landscapes in our paper can be visualized in the Demo with 3D interactive versions.

Citation

@inproceedings{RPP,
author = {Junwon Seo and Sangyoon Lee and Kwang In Kim and Jaeho Lee},
title = {In Search of a Data Transformation That Accelerates Neural Field Training},
year = {2024},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}
}