Pingchuan Ma*, Tao Du*, and Wojciech Matusik

ICML 2020 [Project Page] [Paper] [Video] [Slides]

@inproceedings{ma2020efficient, title={Efficient Continuous Pareto Exploration in Multi-Task Learning}, author={Ma, Pingchuan and Du, Tao and Matusik, Wojciech}, booktitle={International Conference on Machine Learning}, pages={6522--6531}, year={2020}, organization={PMLR} } 

Online demos for MultiMNIST and UCI-Census are available in Google Colab! Try them now!

• Ubuntu 16.04 or higher;
• conda 4.8 or higher.

We will use $ROOT to refer to the root folder where you want to put this project in. We compiled continuous pareto MTL into a package pareto for easier deployment and application.

cd $ROOT git clone https://github.com/mit-gfx/ContinuousParetoMTL.git cd ContinuousParetoMTL conda env create -f environment.yml conda activate cpmtl python setup.py install

After pareto is installed, we are free to call any primitive functions and classes which are useful for Pareto-related tasks, including continuous Pareto exploration. We provide an example for MultiMNIST dataset, which can be found by:

cd multi_mnist

First, we run weighted sum method for initial Pareto solutions:

python weighted_sum.py

The output should be like:

0: loss [2.313036/2.304537] top@1 [7.65%/10.65%] 0: 1/30: loss [1.463346/0.909529] top@1 [51.52%/69.72%] 0: 2/30: loss [0.889257/0.638646] top@1 [71.29%/78.55%] 0: 3/30: loss [0.703745/0.534612] top@1 [77.77%/81.86%] 0: 4/30: loss [0.622291/0.491764] top@1 [80.13%/83.02%] 

Based on these starting solutions, we can run our continuous Pareto exploration by:

python cpmtl.py

The output should be like:

0: 1/10: loss [0.397692/0.350267] top@1 [86.57%/88.11%] 86.37% 86.57% Δ=0.20% absΔ=0.20% 88.10% 88.11% Δ=0.01% absΔ=0.01% 0: 2/10: loss [0.392314/0.351280] top@1 [86.85%/88.07%] 86.37% 86.57% 86.85% Δ=0.28% absΔ=0.48% 88.10% 88.11% 88.07% Δ=-0.04% absΔ=-0.03% 0: 3/10: loss [0.387585/0.352643] top@1 [86.92%/88.03%] 86.37% 86.57% 86.85% 86.92% Δ=0.07% absΔ=0.55% 88.10% 88.11% 88.07% 88.03% Δ=-0.04% absΔ=-0.07% 

Now you can play it on your own dataset and network architecture!

Open up a terminal to launch Jupyter:

cd submission jupyter notebook

You can run the following Jupyter script to reproduce figures in the paper:

fig2.ipynb fig3.ipynb fig4.ipynb fig5_multimnist.ipynb fig5_uci.ipynb 

If you have any questions about the paper or the codebase, please feel free to contact pcma@csail.mit.edu or taodu@csail.mit.edu.