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..
Deep Fractional Fourier Transform
Authors: Hu Yu, Jie Huang, Lingzhi LI, man zhou, Feng Zhao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate the MFRFC on various computer vision tasks, including object detection, image classification, guided super-resolution, denoising, dehazing, deraining, and low-light enhancement. Our proposed MFRFC consistently outperforms baseline methods by significant margins across all tasks. |
| Researcher Affiliation | Collaboration | University of Science and Technology of China 2Alibaba Group |
| Pseudocode | No | The paper includes architectural diagrams (e.g., Figure 3) but no formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is released publicly at https://github.com/yuhuUSTC/FRFT. |
| Open Datasets | Yes | Following [29], the PASCAL VOC 2007 and 2012 training sets [30] are adopted for training... We verify our method on the image classification task with the CIFAR-10 dataset [31]. ...to evaluate our method on the image de-noising task, we employ the widely-used SIDD dataset as training benchmark. ...We verify our method on the image enhancement task with the commonly used dataset, LOL [36]. ...to evaluate our method on the image dehazing task, we employ ITS and SOTS indoor [40] as our training and testing datasets. For validation, we used the widely-used standard benchmark dataset Rain200H, as described in [43]. |
| Dataset Splits | No | The paper mentions using "validation samples from the SIDD dataset" but does not provide specific split percentages or counts for training/validation/test sets across all experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. It only mentions support from a "GPU cluster". |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow, specific libraries). |
| Experiment Setup | No | The paper states, "We re-implement the baseline methods following the settings in the corresponding paper," but does not provide specific hyperparameter values or detailed training configurations for their own method or the re-implemented baselines. |