Official PyTorch implementation of the INR-based codec RECOMBINER (published in ICLR 2024 🌟). It sets a new SOTA on CIFAR-10 at low bitrates and achieves strong performance on other modalities comparing to other INR-based codecs. This repo provides implementations of RECOMBINER across modalities, including image (Kodak, CIFAR-10), audio, video, and protein 3D structure.
Examples of video and protein structures compressed with RECOMBINER. 
We suggest using the following commands.
conda create --name $ENV_NAME conda activate $ENV_NAME conda install pytorch=2.1.0 torchvision=0.16.0 torchaudio=2.1.0 pytorch-cuda=11.8 -c pytorch -c nvidia pip3 install -r requirements.txt Please refer to ./data/Readme.md.
- Training RECOMBINER
python main_prior_training.py [--seed] [--train_dir] [--train_size] [--dataset] [--device] [--max_bitrate] [--saving_dir] - Compression Test Data Points
python main_compression.py [--seed] [--test_dir] [--test_idx] [--dataset] [--device] [--prior_path] [--save_dir] You can also adjust the hyperparameters by modifying config.py
Please consider citing the following paper if you use this repo.
@inproceedings{he2024recombiner, title={RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations}, author={Jiajun He and Gergely Flamich and Zongyu Guo and José Miguel Hernández-Lobato}, booktitle={International Conference on Learning Representations}, year={2024}, }