IGB4MTL

Official implementation of "Improvable Gap Balancing for Multi-Task Learning", which has been accepted to UAI 2023.

Setup environment

conda create -n igb4mtl python=3.8.13
conda activate igb4mtl
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=10.2 -c pytorch

Install the repo:

git clone https://github.com/YanqiDai/IGB4MTL.git
cd IGB4MTL
pip install -r requirement.txt

Run experiment

Follow instruction on the experiment README file for more information regarding, e.g., datasets.

We support our IGB methods and other existing MTL methods with a unified API. To run experiments:

cd experiments/<expirimnet name>
python trainer.py --loss_method=<loss balancing method> --gradient_method=<gradient balancing method>

Here,

• <experiment name> is one of [quantum_chemistry, nyuv2].
• <loss balancing method> is one of igbv1, igbv2 and the following loss balancing MTL methods.
• <gradient balancing method> is one of the following gradient balancing MTL methods.
• Both <loss balancing method> and <gradient balancing method> are optional:
  • only using <loss balancing method> is to run a loss balancing method;
  • only using <gradient balancing method> is to run a gradient balancing method;
  • using neither is to run Equal Weighting (EW) method.
  • using both is to run a combined MTL method by both loss balancing and gradient balancing.

MTL methods

We support the following loss balancing and gradient balancing methods.

Loss Balancing Method (code name)	Paper (notes)
Equal Weighting (ls)	- (linear scalarization)
Random Loss Weighting (rlw)	A Closer Look at Loss Weighting in Multi-Task Learning
Dynamic Weight Average (dwa)	End-to-End Multi-Task Learning with Attention
Uncertainty Weighting (uw)	Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Improvable Gap Balancing v1 (igbv1)	- (our first IGB method)
Improvable Gap Balancing v1 (igbv1)	- (our second IGB method)

Gradient Balancing Method (code name)	Paper (notes)
MGDA (mgda)	Multi-Task Learning as Multi-Objective Optimization
PCGrad (pcgrad)	Gradient Surgery for Multi-Task Learning
CAGrad (cagrad)	Conflict-Averse Gradient Descent for Multi-task Learning
IMTL-G (imtl)	Towards Impartial Multi-task Learning
Nash-MTL (nashmtl)	Multi-Task Learning as a Bargaining Game