Incremental Image De-raining via Associative Memory
Authors: Yi Gu, Chao Wang, Jie Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 luoyi.gy@alibaba-inc.com, lijiecs@sjtu.edu.cn |
| 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. |