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..
Discrimination-aware Channel Pruning for Deep Neural Networks
Authors: Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, Jinhui Zhu
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of our method. For example, on ILSVRC-12, our pruned Res Net-50 with 30% reduction of channels outperforms the baseline model by 0.39% in top-1 accuracy. |
| Researcher Affiliation | Collaboration | Zhuangwei Zhuang1 , Mingkui Tan1 , Bohan Zhuang2 , Jing Liu1 , Yong Guo1, Qingyao Wu1, Junzhou Huang3,4, Jinhui Zhu1 1South China University of Technology, 2The University of Adelaide, 3University of Texas at Arlington, 4Tencent AI Lab |
| Pseudocode | Yes | Algorithm 1 Discrimination-aware channel pruning (DCP). Algorithm 2 Greedy algorithm for channel selection. |
| Open Source Code | Yes | The source code of our method can be found at https://github.com/SCUT-AILab/DCP. |
| Open Datasets | Yes | We evaluate the performance of various methods on three datasets, including CIFAR-10 [20], ILSVRC-12 [4], and LFW [17]. CIFAR-10 consists of 50k training samples and 10k testing images with 10 classes. ILSVRC-12 contains 1.28 million training samples and 50k testing images for 1000 classes. LFW [17] contains 13,233 face images from 5,749 identities. |
| Dataset Splits | No | The paper states the number of training and testing samples for CIFAR-10 and ILSVRC-12 (e.g., 'CIFAR-10 consists of 50k training samples and 10k testing images'). For LFW, it mentions 'ten-fold validation accuracy'. However, it does not explicitly provide details about a specific validation dataset split (e.g., percentages, sample counts, or explicit mention of a validation set beyond what might be implied by 'ten-fold cross validation'). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper states 'We implement the proposed method on Py Torch [32]', but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use SGD with nesterov [30] for the optimization. The momentum and weight decay are set to 0.9 and 0.0001, respectively. We set Ξ» to 1.0 in our experiments by default. On CIFAR-10, we ο¬ne-tune 400 epochs using a mini-batch size of 128. The learning rate is initialized to 0.1 and divided by 10 at epoch 160 and 240. On ILSVRC-12, we ο¬ne-tune the network for 60 epochs with a mini-batch size of 256. The learning rate is started at 0.01 and divided by 10 at epoch 36, 48 and 54, respectively. |