Contrastive Graph Autoencoder for Shape-based Polygon Retrieval from Large Geometry Datasets
Authors: Zexian Huang, Kourosh Khoshelham, Martin Tomko
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | 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. |