Compression with Bayesian Implicit Neural Representations
Authors: Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method achieves strong performance on image and audio compression while retaining simplicity. |
| Researcher Affiliation | Academia | Zongyu Guo University of Science and Technology of China guozy@mail.ustc.edu.cn Gergely Flamich University of Cambridge gf332@cam.ac.uk Jiajun He University of Cambridge jh2383@cam.ac.uk Zhibo Chen University of Science and Technology of China chenzhibo@ustc.edu.cn Jos e Miguel Hern andez-Lobato University of Cambridge jmh233@cam.ac.uk |
| Pseudocode | Yes | Algorithm 1 Learning the model prior |
| Open Source Code | Yes | Our code is available at https://github.com/cambridge-mlg/combiner. |
| Open Datasets | Yes | We evaluate COMBINER on the CIFAR-10 [24] and Kodak [25] image datasets and the Libri Speech audio dataset [26] |
| Dataset Splits | No | The paper describes using a training set to learn the model prior and a test set for evaluation, but does not explicitly define a separate validation split for hyperparameter tuning. |
| Hardware Specification | Yes | encode 500 CIFAR-10 images in parallel with a single A100 GPU |
| Software Dependencies | No | The paper mentions using the 'ffmpeg package' but does not specify versions for core software dependencies like PyTorch or Python. |
| Experiment Setup | Yes | We use a 4-layer MLP with 16 hidden units and 32 Fourier embeddings for the CIFAR-10 dataset. The model prior is trained with 128 epochs... We use the Adam optimizer with learning rate 0.0002. The posterior variances are initialized as 9 ˆ 10 6. ...After obtaining the model prior, given a specific test CIFAR-10 image to be compressed, the posterior of this image is optimized for 25000 iterations, with the same optimizer. |