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..
REPrune: Channel Pruning via Kernel Representative Selection
Authors: Mincheol Park, Dongjin Kim, Cheonjun Park, Yuna Park, Gyeong Eun Gong, Won Woo Ro, Suhyun Kim
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results highlight that REPrune performs better in computer vision tasks than existing methods, effectively achieving a balance between acceleration ratio and performance retention. |
| Researcher Affiliation | Collaboration | 1Yonsei University, Republic of Korea 2Korea University, Republic of Korea 3Korea Institute of Science and Technology, Republic of Korea 4Hyundai MOBIS, Republic of Korea |
| Pseudocode | Yes | Algorithm 1: Channel Selection in REPrune; Algorithm 2: Overview of REPrune |
| Open Source Code | No | The paper mentions using the PyTorch framework but does not provide a link to their own source code for REPrune. |
| Open Datasets | Yes | Datasets and models We evaluate image recognition on CIFAR-10 and Image Net (Deng et al. 2009) datasets and object detection on COCO-2017 (Lin et al. 2014). |
| Dataset Splits | Yes | Image Net validation dataset. |
| Hardware Specification | Yes | While evaluations are conducted on NVIDIA RTX A6000 with 8 GPUs, for the CIFAR-10 dataset, we utilize just a single GPU. Additionally, we employ NVIDIA Jetson TX2 to assess the image throughput of our pruned model with A6000. |
| Software Dependencies | No | The paper states: "This paper evaluates the effectiveness of REPrune using the Py Torch framework", but it does not specify any version numbers for PyTorch or other software dependencies. |
| Experiment Setup | No | The paper states: "Comprehensive details regarding training hyper-parameters and pruning strategies for CNNs are available in the Appendix.", indicating that these details are not provided in the main text. |