VC-BML

This is the code repository of the NeurIPS 2021 paper Variational Continual Bayesian Meta-Learning.

VC-BML is a meta-learning model that deal with online streaming tasks from a non-stationary distribution. It is a fully Bayesian model where both meta-parameters and task-specific parameters are latent random variables. Specifically, the meta-parameters follow a Dynamic Gaussian Mixture model, with the number of component distributions determined by a Chinese Restaurant Process. The posteriors of these latent variables are inferred with structured variational inference.

Requirements

• Python 3.7.9
• pytorch 1.7.1
• numpy
• pandas
• tqdm

Data Setup

• In config/seqdataset.json, specify the location of your custom $DATASET_PATH .
• Download the datasets (Omniglot, CIFAR-FS, mini-Imagenet, VGG-Flowers) and unzip these datasets in your custom $DATASET_PATH. The code assumes the following structure:

$DATASET_PATH
├── omniglot_raw
|   ...
├── cifar100_raw
|   ...
├── mini_imagenet_raw 
|   ...
├── vggflowers_raw 
|   ...

• After downloading the datasets, run the data preparation scripts in the data_generate folder. For example, run the following command to prepare Omniglot for training:

python data_generate/prepare_omniglot.py

Training

Run the training script to train VC-BML:

python traininig/main.py --config_path config/seqdataset.json

Note that details of hyperparameters can be found in the configuration file "seqdataset.json".

Citation

If you find our code useful, please kindly cite the paper:

@article{zhang2021variational,
  title={Variational Continual Bayesian Meta-Learning},
  author={Zhang, Qiang and 
  		  Fang, Jinyuan and 
  		  Meng, Zaiqiao and 
  		  Liang, Shangsong and 
  		  Yilmaz, Emine},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}