Gated Convolutional Networks with Hybrid Connectivity for Image Classification
Authors: Chuanguang Yang, Zhulin An, Hui Zhu, Xiaolong Hu, Kun Zhang, Kaiqiang Xu, Chao Li, Yongjun Xu12581-12588
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on CIFAR and Image Net datasets show that HCGNet is more prominently efficient than Dense Net, and can also significantly outperform state-of-the-art networks with less complexity. |
| Researcher Affiliation | Academia | Chuanguang Yang,1,2 Zhulin An,1 Hui Zhu,1,2 Xiaolong Hu,1,2 Kun Zhang,1,2 Kaiqiang Xu,1,2 Chao Li,1 Yongjun Xu1 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China {yangchuanguang, anzhulin, zhuhui, huxiaolong18g, zhangkun17g, xukaiqiang, lichao, xyj}@ict.ac.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that the source code for the described methodology is publicly available or provide a link. |
| Open Datasets | Yes | We perform extensive experiments across the three highly competitive image classification datasets: CIFAR-10/100 (Krizhevsky and Hinton 2009), and Image Net (ILSVRC 2012) (Deng et al. 2009). |
| Dataset Splits | Yes | Image Net 2012 dataset comprises 1.2 million training images and 50k validation images corresponding to 1000 classes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We employ a stochastic gradient descent (SGD) optimizer with momentum 0.9 and batch size 128. Training is regularized by weight decay 1 10 4 and mixup with α = 1 (Zhang et al. 2017). For HCGNet-A1, we train it for 1270 epochs by SGDR (Loshchilov and Hutter 2016) learning rate curve with initial learning rate 0.1, T0 = 10, Tmul = 2. |