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..
Contrastive Graph Autoencoder for Shape-based Polygon Retrieval from Large Geometry Datasets
Authors: Zexian Huang, Kourosh Khoshelham, Martin Tomko
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we demonstrate this capability based on template query shapes on real-world datasets and show its high robustness to geometric transformations in contrast to existing GAEs, indicating the strong generalizability and versatility of CGAE, including on complex real-world building footprints. |
| Researcher Affiliation | Academia | Zexian Huang, Kourosh Khoshelham & Martin Tomko The University of Melbourne, Parkville, Victoria, 3010, Australia {zexianh@student., k.khoshelham@, tomkom@}unimelb.edu.au |
| Pseudocode | Yes | We depict the algorithmic sequence of CGAE and its relationship with the Equations noted in the main paper in Fig. 6. |
| Open Source Code | Yes | Source code for method implementation and datasets for reproducing experiment results is available at https://github.com/zexhuang/CGAE. |
| Open Datasets | Yes | Source code for method implementation and datasets for reproducing experiment results is available at https://github.com/zexhuang/CGAE. OSM Planet dump, 2023. URL https://planet.osm.org. City of Melbourne 2020 building footprints, May 2021. URL https://data.melbourne.vic.gov.au/explore/dataset/2020-building-footprints/information/. |
| Dataset Splits | Yes | The four Glyph datasets are combined and divided into a training/validation/test set (60 : 20 : 20). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or cloud environment specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer and a cosine annealing schedule but does not provide specific version numbers for software libraries or frameworks used in the implementation. |
| Experiment Setup | Yes | We train all models for 100 epochs with the Adam optimizer (Kingma & Ba, 2015) and an initial learning rate of 0.0001. We set the training batch size b = 32 and apply the same batch size to contrastive loss in CGAE. We set the augmentation ratio r to 20% for both random node dropping and edge perturbation in graph augmentation. |