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An overview of multi-task learning in deep neural networks
S Ruder - ar**s for multi-task learning
Multi-task learning can leverage information learned by one task to benefit the training of
other tasks. Despite this capacity, naively training all tasks together in one model often …
other tasks. Despite this capacity, naively training all tasks together in one model often …
Multi-task learning with deep neural networks: A survey
M Crawshaw - arxiv preprint arxiv:2009.09796, 2020 - arxiv.org
Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …
simultaneously learned by a shared model. Such approaches offer advantages like …
Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning
Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is
becoming an attractive solution in medical image segmentation. To make use of unlabeled …
becoming an attractive solution in medical image segmentation. To make use of unlabeled …
An edge traffic flow detection scheme based on deep learning in an intelligent transportation system
An intelligent transportation system (ITS) plays an important role in public transport
management, security and other issues. Traffic flow detection is an important part of the ITS …
management, security and other issues. Traffic flow detection is an important part of the ITS …
Which tasks should be learned together in multi-task learning?
Many computer vision applications require solving multiple tasks in real-time. A neural
network can be trained to solve multiple tasks simultaneously using multi-task learning. This …
network can be trained to solve multiple tasks simultaneously using multi-task learning. This …
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Neural-based multi-task learning has been successfully used in many real-world large-scale
applications such as recommendation systems. For example, in movie recommendations …
applications such as recommendation systems. For example, in movie recommendations …
Deep model fusion: A survey
Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …
predictions of multiple deep learning models into a single one. It combines the abilities of …