Towards Convolutional Neural Networks Compression via Global Error Reconstruction
Authors: Shaohui Lin, Rongrong Ji, Xiaowei Guo, Xuelong Li
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed GER method is evaluated on the ILSVRC2012 image classification benchmark, with implementations on two widely-adopted convolutional neural networks, i.e., the Alex Net and VGGNet-19. Comparing to several state-of-the-art and alternative methods of CNN compression, the proposed scheme has demonstrated the best rate-distortion performance on both networks. |
| Researcher Affiliation | Collaboration | Shaohui Lin1,2, Rongrong Ji1,2 , Xiaowei Guo3, Xuelong Li4 1Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, 361005, China 2School of Information Science and Engineering, Xiamen University, 361005, China 3Best Image, Tencent Technology (Shanghai) Co.,Ltd, China 4Xi an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi an, China |
| Pseudocode | Yes | Algorithm 1 Alternating optimization layer-by-layer; Algorithm 2 Global error Reconstruction for compressing CNN |
| Open Source Code | No | The paper mentions implementing GER on AlexNet and VGGNet-19, and training using Caffe, but does not provide concrete access to the source code for their proposed GER method. |
| Open Datasets | Yes | We test the proposed GER based CNN compression on the ILSVRC2012 image classification benchmark. The dataset contains more than 1 million training images from 1,000 object classes. |
| Dataset Splits | Yes | The dataset contains more than 1 million training images from 1,000 object classes. It also has a validation set of 50,000 images, where each object class contains 50 images. We randomly select 100,000 images (100 from each class) from the training set for training, and test on the validation set. |
| Hardware Specification | Yes | The compressed networks are trained using Caffe [Y. Jia and Darrell, 2014] and run on NVIDIA GTX TITAN X graphics card with 12GB. |
| Software Dependencies | No | The paper mentions using 'Caffe' for training, but does not provide a specific version number or other software dependencies with versions. |
| Experiment Setup | Yes | The learning rate starts at 0.01 and is halved every 10-epochs; the weight decay is set to 0.0005 and the momentum is set to 0.9. |