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..
Efficient ANN-SNN Conversion with Error Compensation Learning
Authors: Chang Liu, Jiangrong Shen, Xuming Ran, Mingkun Xu, Qi Xu, Yi Xu, Gang Pan
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on CIFAR-10, CIFAR-100, Image Net datasets show that our method achieves high-precision and ultra-low latency among existing conversion methods. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Dalian University of Technology, Dalian, China 2Faculty of Electronic and Information Engineering, Xi an Jiaotong University 3State Key Lab of Brain-Machine Intelligence, Zhejiang University 4National University of Singapore 5Guangdong Institute of Intelligence Science and Technology, Zhuhai, China 6College of Computer Science and Technology, Zhejiang University. Correspondence to: Qi Xu <EMAIL>. |
| Pseudocode | No | The paper describes methods and equations but does not contain explicitly structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Experimental results on CIFAR-10, CIFAR-100, Image Net datasets show that our method achieves high-precision and ultra-low latency among existing conversion methods. |
| Dataset Splits | No | The paper mentions using CIFAR-10, CIFAR-100, and Image Net datasets but does not explicitly provide details about training/test/validation splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory amounts) used for conducting the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In our dual threshold neuron method, the quantization steps L is a hyperparameter that affects the accuracy of the converted SNN. To better understand the impact of L on SNN performance and determine the optimal value, we trained VGG16, Res Net-20, and Res Net-18 networks with a pruning function with a learnable threshold λ using different quantization steps L, including 2, 4, 8, 16, and 32, and then converted them to SNNs. ... this paper sets the negative threshold to a small negative value (-1e-3 according to experience). |