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..
Dual Path Networks
Authors: Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets, Imag Net-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. |
| Researcher Affiliation | Collaboration | National University of Singapore Beijing Institute of Technology National University of Defense Technology Qihoo 360 AI Institute |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not provide any statement or link regarding the open-sourcing of the code for the described methodology. |
| Open Datasets | Yes | Extensive experiments are conducted for evaluating the proposed Dual Path Networks. Specifically, we evaluate the proposed architecture on three tasks: image classification, object detection and semantic segmentation, using three standard benchmark datasets: the Image Net-1k dataset, Places365-Standard dataset and the PASCAL VOC datasets. |
| Dataset Splits | No | The paper mentions using 'validation set' for evaluation (e.g., 'Single crop validation error rate (%) on validation set' in Table 2), but does not explicitly provide the specific percentages or sample counts for the train/validation/test splits, nor does it cite a source defining those exact splits for reproduction within the main text. |
| Hardware Specification | Yes | We implement the DPNs using MXNet [2] on a cluster with 40 K80 graphic cards. |
| Software Dependencies | No | The paper mentions using 'MXNet [2]' for implementation but does not specify the version number of MXNet or any other software dependencies. |
| Experiment Setup | Yes | Following [3], we adopt standard data augmentation methods and train the networks using SGD with a mini-batch size of 32 for each GPU. For the deepest network, i.e. DPN-131, the mini-batch size is limited to 24 because of the 12GB GPU memory constraint. The learning rate starts from 0.1 for DPN-92 and DPN-131, and from 0.4 for DPN-98. It drops in a steps manner by a factor of 0.1. Following [5], batch normalization layers are refined after training. |