This is the official repository to the CVPR 2022 paper "Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning" This repo is based on the training code in iMAML.
You will need Python 3.8 and the packages specified in requirements.txt. We recommend setting up a virtual environment with pip and installing the packages there.
Install packages with:
$ pip install -r requirements.txt
All configurations concerning data, model, training, etc. can be called using commandline arguments.
The implicit_maml script offers many options to train implicit-MAML on 4-conv model family and cifar-fs/miniimagenet dataset.
Here is a sample script to train on cifar-fs dataset, 4-conv model.
python implicit_maml.py
The implicit_maml_pruner script offers many options to prune the pre-trained model at different budgets.
Here is a sample script to prune on cifar-fs dataset, 4-conv model.
python implicit_maml_pruner.py
Please cite our paper in your publications if it helps your research.
@inproceedings{chavan2022metadock,
title={Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning},
author={Chavan, Arnav and Tiwari, Rishabh and Bamba, Udbhav and Gupta, Deepak},
journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
This project is licensed under the MIT License.