Deep Convolutional Neural Networks with Merge-and-Run Mappings
Authors: Liming Zhao, Mingjie Li, Depu Meng, Xi Li, Zhaoxiang Zhang, Yueting Zhuang, Zhuowen Tu, Jingdong Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance on the standard recognition tasks. Our approach demonstrates consistent improvements over Res Nets with the comparable setup, and achieves competitive results (e.g., 3.06% testing error on CIFAR-10, 17.55% on CIFAR-100, 1.51% on SVHN) 1. ... 4 Experiments ... 4.3 Empirical Study ... 4.4 Comparison with State-of-the-Arts |
| Researcher Affiliation | Collaboration | Liming Zhao1, Mingjie Li2, Depu Meng2, Xi Li1 , Zhaoxiang Zhang3 Yueting Zhuang1, Zhuowen Tu4, Jingdong Wang5 1 Zhejiang University 2 University of Science and Technology of China 3 Institute of Automation, Chinese Academy of Sciences 4 UC San Diego 5 Microsoft Research |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/zlmzju/fusenet |
| Open Datasets | Yes | CIFAR-10 and CIFAR-100. The two datasets are drawn from the 80-million tiny image database [Krizhevsky, 2009]. ... SVHN (street view house numbers) dataset... We also compare our DMRNet-50 against the Res Net-98 with the same experimental settings on the Image Net 2012 classification dataset [Deng et al., 2009]. |
| Dataset Splits | No | The paper mentions 50000 training images and 10000 test images for CIFAR-10/100, but does not explicitly provide a separate validation dataset split or its size. |
| Hardware Specification | No | We use SGD with the Nesterov momentum to train all the models for 400 epochs on CIFAR-10/CIFAR-100 and 40 epochs on SVHN, both with a total mini-batch size 64 on two GPUs. |
| Software Dependencies | No | Our implementation is based on MXNet [Chen et al., 2015]. |
| Experiment Setup | Yes | We use SGD with the Nesterov momentum to train all the models for 400 epochs on CIFAR-10/CIFAR-100 and 40 epochs on SVHN, both with a total mini-batch size 64 on two GPUs. The learning rate starts with 0.1 and is reduced by a factor 10 at the 1/2, 3/4 and 7/8 fractions of the number of training epochs. Similar to [He et al., 2016a], the weight decay is 0.0001, the momentum is 0.9, and the weights are initialized as in [He et al., 2015]. |