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..
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 | Venue PDF | 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]. |