Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks
Authors: Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Mahsa Baktashmotlagh, Yang Yang5021-5028
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on the mini Image Net and tiered Image Net datasets demonstrate the effectiveness and efficiency of the proposed method, improving the performance by 42.8% compared with state-of-the-art on the mini Image Net 5-way 1-shot classification task. |
| Researcher Affiliation | Academia | 1The University of Queensland, Australia 2Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China 3Pengcheng Laboratory, Shenzhen, China 4University of Electronic Science and Technology of China, China |
| Pseudocode | Yes | Algorithm 1 Meta-training of the Proposed CML-BGNN. |
| Open Source Code | Yes | Our source code1 is implemented based on Pytorch. ... 1https://github.com/Luoyadan/BGNN-AAAI |
| Open Datasets | Yes | mini Image Net is the subset of the ILSVRC-12 dataset... We follow the class split used by (Ravi and Larochelle 2017)... tiered Image Net is a larger subset of ILSVRC-2012... |
| Dataset Splits | Yes | We follow the class split used by (Ravi and Larochelle 2017), where 64 classes are used for training, 16 for validation, and 20 for testing. |
| Hardware Specification | Yes | All experiments are conducted on a server with two Ge Force GTX 1080 Ti and two GTX 2080 Ti GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch' as the implementation framework, but does not provide specific version numbers for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | The mini-batch size for all graph-based models is 80 and 64 for 1-shot and 5-shot experiments, respectively. The proposed model was trained by Adam optimizer with an initial learning rate η of 1 10 3 and weight decay of 1 10 6. The dropout rate is set to 0.3 and the loss coefficient γ is set to 1. |