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Dec, 2019
使用正交梯度对深度多任务网络进行规范化
Regularizing Deep Multi-Task Networks using Orthogonal Gradients
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Mihai Suteu, Yike Guo
TL;DR
本文提出了一种新的梯度正则化方法,旨在通过强制近似正交梯度来减少任务干扰,评估结果表明该方法在多任务学习中取得了竞争性结果。
Abstract
deep neural networks
are a promising approach towards
multi-task learning
because of their capability to leverage knowledge across domains and learn general purpose representations. Nevertheless, they can fail to
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