https://github.com/qdata/st-maml
https://github.com/qdata/st-maml
Last synced: 11 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/qdata/st-maml
- Owner: QData
- License: apache-2.0
- Created: 2021-11-06T02:13:38.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-05-26T13:49:05.000Z (about 3 years ago)
- Last Synced: 2025-01-11T11:26:34.118Z (over 1 year ago)
- Language: Python
- Size: 2.06 MB
- Stars: 0
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
Code repository for UAI 2022 paper "ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning". [[paper]](https://openreview.net/forum?id=rrlMyPUs9gc)
## Run Experiments
```
# Temperature prediction expriments
cd ST-MAML-Weather
python main.py --method ST_MAML
# Cross dataset image completion experiments
cd ST-MAML-ImgCompletion
python meta_main.py
# Regression fitting
cd ST-MAML-Reg
python python meta_main.py --aug_enc --kl_weight=2.0 --in_weight_rest=0.1 --model_type='prob' --output_folder='results'
For visualization purpose,
python visual.py --aug_enc --kl_weight=2.0 --in_weight_rest=0.1 --model_type='prob' --output_folder='results'
```
## Acknowledgements
The code for ST-MAML is based on [A Closer Look at Few-Shot Classification](https://github.com/wyharveychen/CloserLookFewShot).
## References
Please cite our paper as:
```
@inproceedings{
wang2022stmaml,
title={{ST}-{MAML}: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning},
author={Zhe Wang and Jake Grigsby and Arshdeep Sekhon and Yanjun Qi},
booktitle={The 38th Conference on Uncertainty in Artificial Intelligence},
year={2022},
url={https://openreview.net/forum?id=rrlMyPUs9gc}
}
```