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 [1].
Decoupling the Layers in Residual Networks
Authors: Ricky Fok, Aijun An, Zana Rashidi, Xiaogang Wang
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate through an extensive performance study that the proposed network achieves comparable predictive performance to the original residual network with the same number of parameters, while achieving a significant speed-up on the total training time. |
| Researcher Affiliation | Academia | Ricky Fok , Aijun An, Zana Rashidi Department of Electrical Engineering and Computer Science York University 4700 Keele Street, Toronto, M3J 1P3, Canada EMAIL, EMAIL, EMAIL Xiaogang Wang Department of Mathematics and Statistics York University 4700 Keele Street, Toronto, M3J 1P3, Canada EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper only acknowledges third-party code: 'We thank Wenxin Xu for providing his code for Res Net at https://github.com/wenxinxu/resnet_in_tensorflow.' It does not provide concrete access to the authors' own source code for the methodology described. |
| Open Datasets | Yes | For the CIFAR-10 and CIFAR-100 data sets, we trained for 80000 iterations, or 204 epochs. We also tested Warp Net on a down-sampled (32x32) Image Net data set (Chrabaszcz & Hutter, 2017). |
| Dataset Splits | Yes | For the CIFAR-10 and CIFAR-100 data sets, we trained for 80000 iterations, or 204 epochs. We took a training batch size of 128. Initial learning rate is 0.1. The learning rate drops by a factor of 0.1 at epochs 60, 120, and 160, with a weight decay of 0.0005. The data set contains 1000 classes with 1281167 training images and 50000 validation images with 50 images each class. |
| Hardware Specification | No | The paper mentions 'GPUs' and memory constraints ('requires too much memory on a single GPU') but does not specify exact GPU models, CPU models, or other detailed computer specifications used for experiments. |
| Software Dependencies | No | The paper mentions using 'Tensorflow' for implementation but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | For the CIFAR-10 and CIFAR-100 data sets, we trained for 80000 iterations, or 204 epochs. We took a training batch size of 128. Initial learning rate is 0.1. The learning rate drops by a factor of 0.1 at epochs 60, 120, and 160, with a weight decay of 0.0005. The training batch size is 512, initial learning rate is 0.4 and drops by a factor of 0.1 at every 30 epochs. The weight decay is set to be 0.0001. |