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..
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
Authors: Long Sun, Jinshan Pan, Jinhui Tang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed Shuffle Mixer is about 3 smaller than the state-of-the-art efficient SR methods, e.g. CARN, in terms of model parameters and FLOPs while achieving competitive performance. |
| Researcher Affiliation | Academia | Long Sun, Jinshan Pan , Jinhui Tang Nanjing University of Science and Technology EMAIL |
| Pseudocode | No | The paper describes the network architecture with diagrams but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/sunny2109/Shuffle Mixer. |
| Open Datasets | Yes | Following existing methods [22, 24, 23], we train our models on the DF2K dataset, a merged dataset with DIV2K [37] and Flickr2K [25], which contains 3450 (800 + 2650) high-quality images. |
| Dataset Splits | Yes | Table 2: Ablation studies of the shuffler mixer layer and the feature mixing block on 4 DIV2K validation set[37]. |
| Hardware Specification | Yes | All experiments are conducted with the Py Torch framework on an Nvidia Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'PyTorch framework' but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | In each training mini-batch, we randomly crop 64 patches of size 64 64 from LR images as the input. The proposed model is trained by minimizing L1 loss and the frequency loss [5] with Adam [19] optimizer for 300,000 total iterations. The learning rate is set to a constant 5 10 4. |