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..
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Authors: Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our proposed methods are easy to adopt, stable to train, and highly effective especially on complex compression models. |
| Researcher Affiliation | Academia | Zongyu Guo 1 Zhizheng Zhang 1 Runsen Feng 1 Zhibo Chen 1 1University of Science and Technology of China. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We train the compression models on the full Image Net training set (Deng et al., 2009) and test the rate-distortion performance on Kodak dataset (Kodak, 1993), a widely used dataset for evaluating the performance of image compression model. |
| Dataset Splits | No | The paper mentions training on ImageNet and testing on Kodak, but does not explicitly provide details about a validation dataset split (e.g., percentages, sample counts, or a specific validation set name). |
| Hardware Specification | Yes | All experiments are run on NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for any software dependencies like programming languages, frameworks, or libraries. |
| Experiment Setup | Yes | We strictly follow the settings in (Cheng et al., 2020), including their hyper-parameters (e.g., learning rate and batch size) and network architectures. We train our models with Adam optimizer for 2M iterations. The learning rate is set to 10e-4 initially and decays to 10e-5 at 1.8M iterations. |