Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning

This project is a PyTorch implementation of the Kernel ModuLation (KML) algorithm proposed in Revisit Multimodal Meta-Learning through the lens of Multi-Task Learning, which is published in NeurIPS 2021. Please visit our projrct page for more information.

Multimodal meta-learning is a recent problem that extends conventional few-shot meta-learning by generalizing its setup to diverse multimodal task distributions. This setup mimics how humans make use of a diverse set of prior skills to learn new skills. Previous work has achieved encouraging performance. In particular, in spite of the diversity of the multimodal tasks, previous work claims that a single meta-learner trained on a multimodal distribution can sometimes outperform multiple specialized meta-learners trained on individual unimodal distributions. The improvement is attributed to knowledge transfer between different modes of task distributions. However, there is no deep investigation to verify and understand the knowledge transfer between multimodal tasks. Our work makes two contributions to multimodal meta-learning. First, we propose a method to analyze and quantify knowledge transfer across different modes at a micro-level. Our quantitative, task-level analysis is inspired by the recent transference idea from multi-task learning. Second, inspired by hard parameter sharing in multi-task learning and a new interpretation of related work, we propose a new multimodal meta-learner that outperforms existing work by considerable margins. While the major focus is on multimodal meta-learning, our work also attempts to shed light on task interaction in conventional meta-learning.

Datasets

Run the following command to download and preprocess the datasets

$python download.py --dataset aircraft bird cifar miniimagenet

Or simply download it from here, and put it in data folder.

Getting Started

Training

Use following commands to train the model and reproduce our results:

2Mode (Omniglot & mini-ImageNet)

Setup	Command
5w1s	main.py --multimodal_few_shot omniglot miniimagenet --kml-model True --output-folder kml_2mode_5w1s
5w1s	main.py --multimodal_few_shot omniglot miniimagenet --kml-model True --num-samples-per-class 5 --output-folder kml_2mode_5w5s

2Mode:new: (mini-ImageNet & FC100)

Setup	Command
5w1s	main.py --multimodal_few_shot miniimagenet FC100 --kml-model True --output-folder kml_2mode_dag_5w1s
5w1s	main.py --multimodal_few_shot miniimagenet FC100 --kml-model True --num-samples-per-class 5 --output-folder kml_2mode_dag_5w5s

3Mode (Omniglot, mini-ImageNet & FC100)

Setup	Command
5w1s	main.py --multimodal_few_shot omniglot miniimagenet FC100 --kml-model True --output-folder kml_3mode_5w1s
5w1s	main.py --multimodal_few_shot omniglot miniimagenet FC100 --kml-model True --num-samples-per-class 5 --output-folder kml_3mode_5w5s

5Mode (Omniglot, mini-ImageNet, FC100, CUB & Aircraft)

Setup	Command
5w1s	main.py --multimodal_few_shot omniglot miniimagenet FC100 bird aircraft --kml-model True --output-folder kml_5mode_5w1s
5w1s	main.py --multimodal_few_shot omniglot miniimagenet FC100 bird aircraft --kml-model True --num-samples-per-class 5 --output-folder kml_5mode_5w5s

Evaluating Trained Model

In order to evaluate the trained model, load the saved checkpoint for the desired model in --eval mode.

For example, for 3Mode, 5w1s setup, use the follosing commend:

main.py --multimodal_few_shot omniglot miniimagenet FC100 --kml-model True --checkpoint ./train_dir/kml_5mode_5w1s/model_gatedconv_30000.pt --eval

Cite the paper

If you find this useful, please cite our paper

@article
{abdollahzadeh2021revisit,
  title={Revisit multimodal meta-learning through the lens of multi-task learning},
  author={Abdollahzadeh, Milad and Malekzadeh, Touba and Cheung, Ngai-Man Man},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={14632--14644},
  year={2021}
}

Authors

Milad Abdollahzadeh, Touba Malekzadeh, Ngai-Man (Man) Cheung

Refrences

MMAML

ProtoNet