Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improving Inference for Neural Image Compression
Authors: Yibo Yang, Robert Bamler, Stephan Mandt
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, which include extensive baseline comparisons and ablation studies, we achieve new state-of-the-art performance on lossy image compression using an established VAE architecture, by changing only the inference method. |
| Researcher Affiliation | Academia | Yibo Yang, Robert Bamler, Stephan Mandt Department of Computer Science University of California, Irvine EMAIL |
| Pseudocode | Yes | Algorithm 1: Proposed lossy bits-back coding method (Section 3.3 and Figure 1e-f). |
| Open Source Code | No | The paper does not provide any statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We improve its performance drastically, achieving an average of over 15% BD rate savings on Kodak and 20% on Tecnick [Asuni and Giachetti, 2014]Eastman Kodak. Kodak lossless true color image suite (Photo CD PCD0992). URL http://r0k. us/graphics/kodak.N. Asuni and A. Giachetti. TESTIMAGES: A large-scale archive for testing visual devices and basic image processing algorithms (SAMPLING 1200 RGB set). In STAG: Smart Tools and Apps for Graphics, 2014. URL https://sourceforge.net/projects/testimages/files/OLD/ OLD_SAMPLING/testimages.zip. |
| Dataset Splits | No | The paper uses the Kodak and Tecnick datasets for experiments but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (GPU/CPU models, memory, etc.) used for running its experiments. |
| Software Dependencies | Yes | In all results, we used Adam [Kingma and Ba, 2015] for optimization, and annealed the temperature of SGA by an exponential decay schedule, and found good convergence without per-model hyperparameter tuning. |
| Experiment Setup | Yes | In all results, we used Adam [Kingma and Ba, 2015] for optimization, and annealed the temperature of SGA by an exponential decay schedule, and found good convergence without per-model hyperparameter tuning. We provide details in the Supplementary Material. |