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 | Conference PDF | Archive PDF | Plain Text | 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. |