06_Multi-task Learning
In Computer Vision, CNNs have become the dominant models for vision tasks since 2012. There is an increasing convergence of computer vision and NLP with much more efficient class of architectures.
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0) Overview
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1) Papers:
Multi-task learning
End-to-End Multi-Task Learning with Attention.
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics.
BlitzNet: A Real-Time Deep Network for Scene Understanding.
Triply Supervised Decoder Networks for Joint Detection and Segmentation
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving.
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation.
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Network
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems
MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery
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