ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
Authors: Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the benefits of our Expand Nets on image classification, object detection, and semantic segmentation. We further provide an ablation study to analyze the influence of different expansion strategies and expansion rates in the supplementary material. |
| Researcher Affiliation | Collaboration | Shuxuan Guo CVLab, EPFL Lausanne 1015, Switzerland shuxuan.guo@epfl.ch Jose M. Alvarez NVIDIA Santa Clara, CA 95051, USA josea@nvidia.com Mathieu Salzmann CVLab, EPFL Lausanne 1015, Switzerland mathieu.salzmann@epfl.ch |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our code is available at https://github.com/GUOShuxuan/expandnets. |
| Open Datasets | Yes | For CIFAR-10 and CIFAR-100 [27], we use the same compact network as in [37]... For Image Net [46], we use the compact Mobile Net [21]... YOLO-LITE [23]... trained... on the PASCAL VOC2007 + 2012 [12, 13] training and validation sets... Cityscapes dataset [8]. |
| Dataset Splits | Yes | trained the resulting network on the PASCAL VOC2007 + 2012 [12, 13] training and validation sets in the standard YOLO fashion [39, 40]. |
| Hardware Specification | Yes | Epoch Time was evaluated on CIFAR-10 on 2 32G TITAN V100 GPUs. |
| Software Dependencies | No | No specific version numbers for software dependencies (e.g., PyTorch 1.9) were explicitly provided in the paper. It only mentions 'a pytorch implementation'. |
| Experiment Setup | Yes | We train for 350 epochs using a batch size of 128. We use stochastic gradient descent (SGD) with a momentum of 0.9, weight decay of 0.0005 and a learning rate of 0.1, divided by 10 at epochs 150 and 250. |