Multi-Task Learning
690 papers with code • 3 benchmarks • 42 datasets
Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.
( Image credit: Cross-stitch Networks for Multi-task Learning )
Benchmarks
These leaderboards are used to track progress in Multi-Task Learning
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find Multi-Task Learning models and implementationsDatasets
Cityscapes
CelebA
KP20k
CELEX
Aff-Wild2
Clotho
ETHICS
Most implemented papers
RetinaFace: Single-stage Dense Face Localisation in the Wild
Face Analysis Project on MXNet
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
ifzhang/FairMOT
•
•
Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives.
Towards Real-Time Multi-Object Tracking
Zhongdao/Towards-Realtime-MOT
•
•
ECCV 2020
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Language Models are Unsupervised Multitask Learners
PaddlePaddle/PaddleNLP
•
•
Preprint 2019
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
Gradient Surgery for Multi-Task Learning
tianheyu927/PCGrad
•
•
NeurIPS 2020
While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge.
You Only Learn One Representation: Unified Network for Multiple Tasks
WongKinYiu/yolor
•
•
In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning.
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning
Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments.
