CMVAE: Causal Meta VAE for Unsupervised Meta-Learning
Authors: Guodong Qi, Huimin Yu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on toy datasets and three benchmark datasets demonstrate that our method can remove the context-bias and it outperforms other state-of-the-art unsupervised meta-learning algorithms because of bias-removal. Code is available at https://github.com/Guodong Qi/CMVAE. |
| Researcher Affiliation | Academia | Guodong Qi 1,2, Huimin Yu 1,2,3,4 1College of Information Science and Electronic Engineering, Zhejiang University 2ZJU-League Research & Development Center 3State Key Lab of CAD&CG, Zhejiang University 4Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking {guodong qi, yhm2005}@zju.edu.cn |
| Pseudocode | Yes | Algorithm 1: Unsupervised Causal Meta-training |
| Open Source Code | Yes | Code is available at https://github.com/Guodong Qi/CMVAE. |
| Open Datasets | Yes | Omniglot, mini Image Net, Celeb A. The paper references 'mini Image Net. It is a subset of Image Net (Russakovsky et al. 2015)...' |
| Dataset Splits | Yes | We take 1200, 100, 323 classes for training, validation and test, respectively. (Omniglot) ... we take 64 classes for training, 16 for validation and 20 for test, respectively. (mini Image Net) |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for experiments, such as specific GPU or CPU models. It only mentions general training parameters and networks. |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma and Ba 2015)' as an optimizer but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The number of iterations for causal-EM steps of all experiment is 10. The hyper-parameters γ, λ1 and λ2 are chosen based on the validation accuracy. We train all models for 60,000 iterations using Adam (Kingma and Ba 2015). |