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..
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
Authors: David Minnen, Johannes Ballé, George D. Toderici
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our generalized models by calculating the rate distortion (RD) performance averaged over the publicly available Kodak image set [21]2. Figure 2 shows RD curves using peak signalto-noise ratio (PSNR) as the image quality metric. [...] The combined model yields state-of-the-art rate distortion performance and generates smaller files than existing methods: 15.8% rate reductions over the baseline hierarchical model and 59.8%, 35%, and 8.4% savings over JPEG, JPEG2000, and BPG, respectively. |
| Researcher Affiliation | Industry | David Minnen, Johannes Ballé, George Toderici Google Research EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode blocks or sections explicitly labeled 'Algorithm'. |
| Open Source Code | No | The paper does not provide an explicit statement about making its source code publicly available or include any links to a code repository. |
| Open Datasets | Yes | We evaluate our generalized models by calculating the rate distortion (RD) performance averaged over the publicly available Kodak image set [21]2. |
| Dataset Splits | No | The paper mentions training and evaluating on datasets, but it does not specify explicit percentages or counts for training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not specify any details about the hardware (e.g., GPU models, CPU specifications, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions, or specific libraries). |
| Experiment Setup | Yes | Details about the individual network layers in each component of our models are outlined in Table 1. [...] Optimized with λ = 0.025 (bpp 0.61 on Kodak), the baseline outperforms the other variants we tested (see text for details). |