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..
Flexible Residual Binarization for Image Super-Resolution
Authors: Yulun Zhang, Haotong Qin, Zixiang Zhao, Xianglong Liu, Martin Danelljan, Fisher Yu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments and comparisons with recent leading binarization methods. Our proposed baselines, FRBC and FRBT, achieve superior performance both quantitatively and visually. 4. Experiments |
| Researcher Affiliation | Academia | 1Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China 2ETH Z urich, Switzerland 3Beihang University, China. |
| Pseudocode | Yes | Algorithm 1 Flexible Residual Binarization for Image SR |
| Open Source Code | No | The paper does not provide any explicit statements about releasing code or links to a code repository for the methodology described. |
| Open Datasets | Yes | Following the common practice (Lim et al., 2017; Zhang et al., 2018a), we adopt DIV2K (Timofte et al., 2017) as the training data. |
| Dataset Splits | No | The paper mentions DIV2K as training data and five benchmark datasets for testing, but does not explicitly describe a validation dataset split or how training data is partitioned for validation. |
| Hardware Specification | Yes | Py Torch (Paszke et al., 2017) is employed to conduct all experiments with NVIDIA RTX A6000 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2017)' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | In the training phase, same as previous work (Lim et al., 2017; Zhang et al., 2018a; Xin et al., 2020; Liang et al., 2021), we conduct data augmentation (random rotation by 90 , 180 , 270 and horizontal flip). We train the model for 300K iterations. Each training batch extracts 32 image patches, whose size is 64 64. We utilize Adam optimizer (Kingma & Ba, 2015) (β1=0.9, β2=0.999, and ϵ=10 8) during training. The initial learning rate 2 10 4, which is reduced by half at the 250K-th iteration. |