Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network
Authors: Saihui Hou, Zilei Wang8425-8432
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | WCD with VGGNet16, Res Net-101, Inception-V3 are experimentally evaluated on multiple datasets. The extensive results demonstrate that WCD can bring consistent improvements over the baselines. |
| Researcher Affiliation | Academia | Saihui Hou, Zilei Wang Department of Automation, University of Science and Technology of China saihui@mail.ustc.edu.cn, zlwang@ustc.edu.cn |
| Pseudocode | Yes | Algorithm 1 Weighted Random Selection Input: scorei > 0, maski = 0, i = 1, 2, , N, wrs ratio. Output: maski, i = 1, 2, , N. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing its source code or a link to a repository. |
| Open Datasets | Yes | CUB-200-2011 (Wah et al. 2011), Stanford Cars (Krause et al. 2013), and Caltech-256 (Griffin, Holub, and Perona 2007) are all well-known public datasets used and properly cited. |
| Dataset Splits | Yes | The hyper-parameters including wrs ratio and q are set by cross validation and keep consistent on the similar datasets such as CUB-200-2011 and Stanford Cars. |
| Hardware Specification | Yes | All the models are implemented with Caffe (Jia et al. 2014) on Titan-X GPUs. |
| Software Dependencies | No | The paper mentions 'Caffe (Jia et al. 2014)' but does not provide a specific version number for Caffe or any other software dependencies. |
| Experiment Setup | Yes | The initial learning rate is set to 0.001 and reduces to its 1/10 three times until convergence. Stochastic gradient descent (SGD) is used for the optimization. The hyper-parameters including wrs ratio and q are set by cross validation and keep consistent on the similar datasets such as CUB-200-2011 and Stanford Cars. |