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..
Learning Scale-Aware Spatio-temporal Implicit Representation for Event-based Motion Deblurring
Authors: Wei Yu, Jianing Li, Shengping Zhang, Xiangyang Ji
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our SASNet outperforms stateof-the-art methods on both synthetic Go Pro and real H2D datasets, especially in high-speed motion scenarios. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China 2School of Computer Science, Peking University, Beijing, China. 3Department of Automation, Tsinghua University, Beijing, China. |
| Pseudocode | No | The paper provides figures of network architectures (Figure 2, 3, 4) and describes the method in text, but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and dataset are available at https://github.com/aipixel/SASNet. |
| Open Datasets | Yes | Code and dataset are available at https://github.com/aipixel/SASNet. |
| Dataset Splits | Yes | Go Pro Dataset. It consists of 3214 sharp images with resolutions of 1280 720, in which 2103 are used for training and 1111 for testing. |
| Hardware Specification | Yes | The proposed SASNet is implemented by Py Torch and trained on an NVIDIA Ge Force RTX 3090 for 100 epochs with 8 batch sizes. |
| Software Dependencies | No | The proposed SASNet is implemented by Py Torch... In Py Torch (Paszke et al., 2019)... |
| Experiment Setup | Yes | The training patch size is set to 256 256 and augmented by horizontal and vertical flipping to enhance its robustness. We use the Adam optimizer (Kingma & Ba, 2014) with an initial learning rate of 10 4 that linear decays by 0.5 for every 30 epoch and only employ L1 loss as the training loss. |