Deep Fractional Fourier Transform
Authors: Hu Yu, Jie Huang, Lingzhi LI, man zhou, Feng Zhao
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |