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..
Hybrid Graph Neural Networks for Few-Shot Learning
Authors: Tianyuan Yu, Sen He, Yi-Zhe Song, Tao Xiang3179-3187
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our HGNN obtains new state-of-the-art on three FSL benchmarks. |
| Researcher Affiliation | Collaboration | 1Center for Vision, Speech and Signal Processing, University of Surrey 2National University of Defense Technology 3i Fly Tek-Surrey Joint Research Centre on Arti๏ฌcial Intelligence |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and models are available at https://github.com/Tianyuan Yu/HGNN. |
| Open Datasets | Yes | Three widely used FSL benchmarks, Mini Image Net (Vinyals et al. 2016), Tiered Image Net (Ren et al. 2018) and CUB-200-2011 (Wah et al. 2011) are used in our experiments. |
| Dataset Splits | Yes | Mini Image Net... consisting of 64 classes for training, and 16 classes and 20 classes for validation and testing respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or processor types used for running experiments. |
| Software Dependencies | No | The paper mentions the use of various models and networks (e.g., CNNs, GNNs) but does not provide specific software dependencies or library version numbers required for replication. |
| Experiment Setup | No | The paper describes the overall training objectives, model architecture, and general training process (e.g., use of cross-entropy losses, end-to-end training, meta-learning setup) but does not provide concrete numerical values for hyperparameters like learning rate, batch size, or number of epochs. |