DNR-Pruning: Sparsity-Aware Pruning via Dying Neuron Reactivation in Convolutional Neural Networks
Authors: Boyuan Wang, Richard Jiang
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on diverse datasets demonstrate that DNRPruning outperforms existing sparsity-aware pruning techniques while achieving competitive results compared to state-of-the-art methods. These findings suggest that dying neurons can serve as an efficient mechanism for network compression and resource optimization in CNNs, opening new avenues for more efficient and high-performance deep learning models. |
| Researcher Affiliation | Academia | Boyuan Wang EMAIL LIRA Centre University of Lancaster Richard Jiang EMAIL LIRA Center University of Lancaster |
| Pseudocode | Yes | Algorithm 1 DNR-Pruning Algorithm |
| Open Source Code | Yes | Codes, data and more results can be found at https://github.com/wangbst/DNR-Pruning/. |
| Open Datasets | Yes | We trained MLPNet, Res Net-18 and VGG-16 networks on MNIST, CIFAR-10 and Tiny-Image Net. |
| Dataset Splits | No | The paper mentions using well-known datasets like MNIST, CIFAR-10, Tiny-Image Net, and Image Net for experiments. However, it does not explicitly state the specific training/validation/test splits, percentages, or methodology used for these datasets beyond implying standard usage. For example, it does not mention '80/10/10 split' or 'standard splits from [citation]'. |
| Hardware Specification | Yes | We used 30G bytes of memory, an NVidia V100 GPU and an Intel(R) Xeon(R) Platinum 8352Y CPU @ 2.20GHz CPU. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with their version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA x.x). |
| Experiment Setup | Yes | We trained MLPNet, Res Net-18 and VGG-16 networks on MNIST, CIFAR-10 and Tiny-Image Net. Following the training scheme outlined by Evci et al Evci (2020). Based on the results in Table 7, our proposed method, DNR-Pruning, achieves the highest Top-1 accuracy among all compared pruning approaches on Res Net-56 with Image Net, including a recent SOTA method published at WACV 2024. Notably, it reaches this performance within only 90 training epochs... |