Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network
Authors: Saihui Hou, Zilei Wang8425-8432
AAAI 2019 | Venue PDF | 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 EMAIL, EMAIL |
| 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 (Grifο¬n, 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. |