是的。但是不是针对不同的loss优化的。所有的loss是一起优化的。GradNorm的作用就是就是根据这些结果调整模型中计算图的梯度。在多任务学习中,传统的方法就是将所有任务的loss相加,只其加权值进行反向传播,并优化参数。 03-05· 美国 回复喜欢展开其他 1 条回复 关于我独自升级 作者 有不理解的地方,...
笔记:GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks 2 年前 路痴斯基关注对于多目标任务学习的损失函数: L(t)=Σwi(t)Li(t) ,由于不同的任务的损失函数的梯度值在尺度上的不同,导致训练过程中某些任务或者某个单独任务可能会占据梯度训练的主导地位,而其他任务的梯度...
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State-of-the-art methods improve the accuracy of both instance recognition and pose estimation using multitask learning. These methods use unified balancing parameters to integrate the loss of each task, which means task difficulties are the same for all objects. However, the method we propose ...
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