Entropy Induced Pruning Framework for Convolutional Neural Networks
Authors: Yiheng Lu, Ziyu Guan, Yaming Yang, Wei Zhao, Maoguo Gong, Cai Xu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement our AFIE-based pruning method for three popular CNN models of Alex Net, VGG-16, and Res Net-50, and test them on three widely-used image datasets MNIST, CIFAR-10, and Image Net, respectively. The experimental results are encouraging. |
| Researcher Affiliation | Academia | Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi an, China lyhxdu@gmail.com, zyguan@xidian.edu.cn, yym@xidian.edu.cn, ywzhao@mail.xidian.edu.cn, gong@ieee.org, cxu@xidian.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The methodology is described using text and equations, and Figure 1 is a diagram, not pseudocode. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There are no links or explicit statements about code release. |
| Open Datasets | Yes | We implement our AFIE-based pruning method for three popular CNN models of Alex Net, VGG-16, and Res Net-50, and test them on three widely-used image datasets MNIST, CIFAR-10, and Image Net, respectively. |
| Dataset Splits | No | The paper mentions 'training epochs' and 'test' results, but does not provide specific train/validation/test dataset splits needed to reproduce the experiment. It does not mention a validation set explicitly or how it was partitioned. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. It mentions using CNNs but no underlying hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Specifically, we set the training epochs as 1 and 150 for VGG-16 and Res Net-50, as well as 1 and 20 for Alex Net. ... We set the overall pruning ratio λ to 70%, and specify the specific pruning ratio for each layer according to the Equation (8). ... the overall pruning ratio λ is set as 65%, ... When we set the overall pruning ratio λ to 30%, the pruning ratios of Conv1, Conv2, and Conv3 can be set to 17%, Conv4, Conv5, Conv6, and Conv7 can be set to 29%, Conv8, Conv9, Conv10, Conv11, Conv12, and Conv13 can be set to 51%, and Conv14, Conv15, and Conv16 can be specified as 90% according to Equation (10). |