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..
Towards Efficient Image Compression Without Autoregressive Models
Authors: Muhammad Salman Ali, Yeongwoong Kim, Maryam Qamar, Sung-Chang Lim, Donghyun Kim, Chaoning Zhang, Sung-Ho Bae, Hui Yong Kim
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train all our models on the Vimeo-90k dataset Xue et al. [2019]... We tested our model on a commonly used Kodak lossless images dataset Kodak [1993]... Figure 1: Performance-complexity tradeoff using various entropy models... Figure 7: RD rate comparison... Table 1: Average encoding and decoding time... Ablation Studies: Comprehensive ablation studies regarding various mask types, mask sizes, and α values are presented in the supplementary material. |
| Researcher Affiliation | Academia | Muhammad Salman Ali 1, Yeongwoong Kim1, Maryam Qamar 1, Sung-Chang Lim2, Donghyun Kim2, Chaoning Zhang1, Sung-Ho Bae 1, Hui Yong Kim 1 1 Kyung Hee University, Republic of Korea 2 Electronics and Telecommunications Research Institute (ETRI), Republic of Korea |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, 'We perform all our experiments on the Pytorch framework Paszke et al. [2017] and use the Compress AI library Bégaint et al. [2020].' but does not provide an explicit statement or link for the authors' own source code for the methodology described. |
| Open Datasets | Yes | We train all our models on the Vimeo-90k dataset Xue et al. [2019]... We tested our model on a commonly used Kodak lossless images dataset Kodak [1993]... |
| Dataset Splits | No | The paper states it trains on the Vimeo-90k dataset and tests on the Kodak dataset, but it does not specify a separate validation split for either dataset. |
| Hardware Specification | Yes | Minnen s and Cheng s models were trained using an NVIDIA 2080Ti, whereas the Swin T model was trained on an NVIDIA 3070Ti due to the transformers high memory requirement. |
| Software Dependencies | No | The paper mentions 'Pytorch framework Paszke et al. [2017] and use the Compress AI library Bégaint et al. [2020]' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The models were optimized using the Adam optimizer Kingma and Ba [2015] with a batch size of 16 and trained for 1.5 million iterations with a learning rate of 1 10 4 for the first million iterations and then halved every 50,000 iterations till 1.25 million iterations. ... The rate-distortion tradeoff is guided by λ, whose value is contained in the set [0.0009, 0.0018, 0.0035, 0.0067, 0.0130, 0.0250]. |