Asymmetric Deep Multi-Task Learning

Abstract

Recent developments make deep neural networks a valuable asset for autonomous driving. They can be deployed as an end-to-end system or part of more complex systems for specific tasks. If a system needs several tasks by neural networks, using multi-task learning (MTL) introduces few benefits compared to deploying several single-task learning (STL) models, such as better time and space complexity on deployment and potentially increased generalization on the backbone network. However, MTL often faces unique challenges. Many existing MTL datasets have limited labels or lack the required labels for specific tasks, and generating labels for these tasks leads to resource and time consumption for researchers. Training the model on an asymmetric labeled dataset, a dataset where labels for specific tasks are unavailable for a subset, can cause a biased gradient, reflecting an unbalance in the accuracy of tasks. In this thesis, asymmetric MTL were investigated and compared to symmetric MTL and STL methods.