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..
Efficient Mirror Detection via Multi-Level Heterogeneous Learning
Authors: Ruozhen He, Jiaying Lin, Rynson W.H. Lau
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared to the state-of-the-art method, Het Net runs 664% faster and draws an average performance gain of 8.9% on MAE, 3.1% on Io U, and 2.0% on F-measure on two mirror detection benchmarks. We conduct experiments on two datasets: MSD (Yang et al. 2019) and PMD (Lin, Wang, and Lau 2020). |
| Researcher Affiliation | Academia | Department of Computer Science, City University of Hong Kong EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its method in text and with mathematical formulas, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Catherine-R-He/Het Net. |
| Open Datasets | Yes | We conduct experiments on two datasets: MSD (Yang et al. 2019) and PMD (Lin, Wang, and Lau 2020). |
| Dataset Splits | No | The paper specifies training and testing sets for the datasets but does not explicitly detail a validation set split or methodology for it. |
| Hardware Specification | Yes | We implement our model by Py Torch and conduct experiments on a Ge Force RTX2080Ti GPU. |
| Software Dependencies | No | The paper states 'We implement our model by Py Torch' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use the stochastic gradient descent (SGD) optimizer with a momentum value of 0.9 and a weight decay of 5e-4. In the training phase, the maximum learning rate is 1e-2, the batch size is 12, and the training epoch is 150. |