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MTLA - Multi-Task Learning Archive

Welcome to our Multi-Task Learning Archive Repository! This comprehensive collection houses a diverse range of cutting-edge multi-task learning algorithms, models, datasets, and research implementations. Whether you're a researcher, developer, or enthusiast exploring the realms of machine learning, this repository offers a rich resource pool to delve into multi-task learning techniques across various domains. Explore, experiment, and advance your understanding of simultaneous learning paradigms with our curated collection of resources and tools.

Environment Setup

After cloning this repository, cd inside and use the following commands to create a virtual environment

python -m venv .env
source .env/bin/activate
python -m pip install -U pip

The requirement packages for this repo are listed below, though pip install wheel is recommended to run first

albumentations==1.3.1
fastparquet==2023.10.1
pandas==2.1.3
tensorboard==2.15.1
tqdm==4.66.1
wandb==0.16.0
scikit-learn==1.3.2

The final thing is to install Pytorch (This repository is tested using Ubuntu 22.04, CUDA 12.1, and NVIDIA driver 523s)

pip3 install torch torchvision torchaudio

Repository Information

Current Available Datasets

Dataset Mode Status Related Task
Oxford Pet III - Available Segmentation (3 classes), Classification (37 classes)
NYUV2 - Available Segmentation (19 classes), Depth Estimation, Surface Normal
Cityscape fine Available Segmentation (19 classes), Depth Estimation
Cityscape coarse Available Segmentation (19 classes), Depth Estimation
CelebA - Available (40+) Attibute Classification (binary labelled), Deep Metric Learning (10k+ identity), Resconstruction (250k+ images), Disentanglement Learning

Current Available Methods

Method Code Status
Gradient Normalization gn Available
Uncertainty Weighting uw Available
Dynamic Weight Average dwa Available
Random Loss Weighting rlw Available
MGDA mgda Available
PCGRAD pcgrad Available
CAGRAD cagrad Available
Recon recon -
NashMTL nash -
Geometric Loss Strategy geo -
Gradient Sign Dropout gsd -
IMTL imtl -
Gradient Vaccine gvac -
MoCo moco -
Aligned MTL amtl -

Current Available Model Architecture

Based Architecture Mode Dataset Available
Unet Hard Parameter Sharing OxfordPetIII
SegNet Hard Parameter Sharing OxfordPetIII

Training

Using the parameter in main.py to perform a customized training process. The experiment evaluation (i.e. loss value, metrics value) is recorded by toggling --log and --wandb.