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..
Incremental Image De-raining via Associative Memory
Authors: Yi Gu, Chao Wang, Jie Li
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our method can achieve better performance than existing approaches on both inhomogeneous and incremental datasets within the spectrum of highly compact systems. |
| Researcher Affiliation | Collaboration | 1 Alibaba Cloud Computing Ltd. 2 Department of Computer Science and Engineering, Shanghai Jiao Tong University EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods textually and with mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate all incremental de-raining methods on four benchmark datasets: Rain100L (Yang et al. 2017), Rain100H (Yang et al. 2017), Rain800 (Zhang, Sindagi, and Patel 2019) and Rain1400 (Fu et al. 2017c). |
| Dataset Splits | No | The paper states, 'Following the previous work, we partition training and testing samples of each dataset according to the existing split.' However, it does not explicitly provide details for a validation split (percentages, counts, or explicit reference to where these splits are defined if not standard). |
| Hardware Specification | Yes | All experiments are conducted on NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used. |
| Experiment Setup | No | The paper mentions keeping 'all training techniques and parameters setting consistent with implementations in original papers for a fair comparison,' but it does not explicitly list concrete hyperparameter values or training configurations within its own text. |