【论文】Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
本文提出了一种多任务loss权重学习的方式，通过训练协调了三个任务的loss，有语义分割、实力分割和depth regression，结构主要是： 文中主要分为了三种情况来介绍： 回归任务 定义一个概率分布函数，假设其符合高斯分布，令 f W ( x ) f^W(x) fW(x)为输入为x、权重为W的网络的输出，并将 f W ( x ) f^W(x) fW(x)看作均值 μ \mu μ，则有： p
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相关文章
- 1. 【Loss】Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
- 2. Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
- 3. Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics 多任务学习
- 4. 多任务学习“Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics”
- 5. multitask learning 相关论文资源
- 6. 论文翻译：2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
- 7. Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics 之论文详解
- 8. 论文：Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo
- 9. 3D Bounding Box Estimation Using Deep Learning and Geometry论文理解
- 10. 3D Bounding Box Estimation Using Deep Learning and Geometry