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..
DeepFacade: A Deep Learning Approach to Facade Parsing
Authors: Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method by training a FCN-8s network with the novel loss function. Experimental results show that our method has outperformed previous state-of-the-art methods significantly on both the ECP dataset and the e TRIMS dataset. |
| Researcher Affiliation | Collaboration | 1 College of Computer Science, Zhejiang University, China 2Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies 3School of Information Systems, Singapore Management University, Singapore |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate our porposed approach on two different datasets, the Ecole Centrale Paris (ECP) Facades dataset [Teboul et al., 2010] and the e TRIMS [Korˇc and Förstner, 2009] database. |
| Dataset Splits | Yes | 5-fold cross-validation is taken to generate proposals from all images. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'VGG16', 'Faster R-CNN', 'RPN', and 'Adam optimizer' but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | The initial learning rate is set to 10 6. For the ECP dataset, training epoch is 100 and for the e TRIMS dataset training epoch is 80. ... Dropout [Srivastava et al., 2014] is used during training to prevent overfitting. ... For η in Equation 8, we set it to be 0.17 empirically. |