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..
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Authors: Sen Deng, Yidan Feng, Mingqiang Wei, Haoran Xie, Yiping Chen, Jonathan Li, Xiao-Ping Zhang, Jing Qin
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate the proposed approach in three representative datasets and experimental results corroborate our approach consistently outperforms state-of-the-art deraining algorithms. In this section, we evaluate our method on three synthetic datasets: Rain200L, Rain200H [Yang et al., 2017] and Rain800 [Zhang et al., 2019]. |
| Researcher Affiliation | Academia | 1Nanjing University of Aeronautics and Astronautics, Nanjing, China 2Lingnan University, Hong Kong, China 3Xiamen Univeristy, Xiamen, China 4Ryerson University, Toronto, Canada 5Hong Kong Polytechnic University, Hong Kong, China |
| Pseudocode | No | The paper describes the architecture and components verbally and visually (Figure 2) but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | There is no explicit statement about releasing the code for the described method, nor any links to a code repository. |
| Open Datasets | Yes | In this section, we evaluate our method on three synthetic datasets: Rain200L, Rain200H [Yang et al., 2017] and Rain800 [Zhang et al., 2019]. |
| Dataset Splits | No | The paper mentions using 'three synthetic datasets' but does not specify the train/validation/test splits, percentages, or absolute counts for any of them. It refers to 'evaluation' but not explicit split details. |
| Hardware Specification | No | The paper does not mention any specific hardware (GPU, CPU models, etc.) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper discusses the network architecture and loss functions but does not provide specific training hyperparameters such as learning rate, batch size, number of epochs, or optimizer details. |