Method Details

Details for method 'Multitask Learning'


Method overview

name Multitask Learning
challenge instance-level semantic labeling
details Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task's loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task.
publication Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Alex Kendall, Yarin Gal and Roberto Cipolla
CVPR 2018
project page / code
used Cityscapes data fine annotations
used external data ImageNet
runtime n/a
subsampling no
submission date April, 2018
previous submissions


Average results

Metric Value
AP 21.5729
AP50% 39.0098
AP100m 35.0005
AP50m 37.0428


Class results

Class AP AP50% AP100m AP50m
person 19.2287 38.1028 38.1629 38.8931
rider 21.3894 46.2562 35.5064 36.3393
car 36.5683 54.7505 60.4266 64.0127
truck 18.7961 28.4405 26.6905 29.0803
bus 26.8213 40.778 42.6905 46.6384
train 15.8773 25.0182 24.4086 28.188
motorcycle 19.3886 42.2056 27.4564 28.5165
bicycle 14.5134 36.5269 24.6618 24.6738



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