Multi-Task-Learning

This is an implementation of exploiting the generalized mean for per-task loss aggregation in multi-task learning. Our code is mainly based on LibMTL.

Getting started

1. Create a virtual environment

   conda create -n gemtl python=3.8
   conda activate gemtl
   pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

2. Clone this repository

3. Install LibMTL

   cd GeMTL
   pip install -e .

Requirements

• Python >= 3.8
• Pytorch >= 1.8.1

pip install -r requirements.txt

Dataset

You can download datasets in the following links.

• NYUv2
• Office-Home

Run

Training and testing codes are in ./examples/{nyusp, office}/main.py.
You can check the results by running the following command.

cd ./examples/{nyusp, office}
bash run.sh

Reference

Our implementation is developed on the following repositories. Thanks to the contributors!

• LibMTL
• CAGrad
• mtan

License

This repository is released under the MIT license.