单任务学习(single task learning):一个loss,一个任务,例如NLP里的情感分类、NER任务一般都是可以叫单任务学习。 多任务学习(multi task learning):简单来说有多个目标函数loss同时学习的就算多任务学习。例如现在大火的短视频,短视频APP在向你展示一个大长腿/帅哥视频之前,通常既要预测你对这个视频感兴趣/不感兴趣...
本文译自Deep Multi-Task Learning – 3 Lessons Learned by Zohar Komarovsky 在过去几年里,Multi-Task Learning (MTL)广泛用于解决多个Taboola(公司名)的业务问题。在这些业务问题中, 人们使用一组相同的特征以及深度学习模型来解决MTL相关问题。在这里简单分享一下我们做MTL时学习到的一些小知识。 小知识第一条:...
Self-supervised Auxiliary Learning: Learning to X by YAuxiliary learning 还可以应用在 self-supervised learning 中，假设对于 primary task 和 auxiliary task 都没有任何 human label。这里的 self-supervised training 跟跟 supervised auxiliary task 一样，是有效得利用了人类先验知识对于部分任务组合的特性。较...
幸运的是,有一篇论文《Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics》通过“不确定性(uncertainty)”来调整损失函数中的加权超参,使得每个任务中的损失函数具有相似的尺度。该算法的keras版本实现,详见github。 调整学习率 learning rate 在神经网络的参数中,learning rate是...
A multi-task learning (MTL) framework. It shares the same encoder across multiple decoders. These decoders can have dependencies on each other which will be properly handled during decoding. To integrate a component into this MTL framework, a component needs to implement the Task interface....
概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss function, you are effectively doing multi-task learning (in contrast to single-task learning))。在那种场景中,这样做有利于想清楚我们真正要做的是什么...
Loss FunctionMulti-task learning (MTL) is a popular method in machine learning which utilizes related information of multi tasks to learn a task more efficiently and accurately. Naively, one can benefit from MTL by using a weighted linear sum of the different tasks loss functions. Manual ...
Multi-task learning 中的Loss设计 核心问题: 在Multi-task learning的一个核心的问题是loss的设计: 1、如何控制各子任务loss的权重? 2、起始训练时各子任务loss的数量级不同会对收敛造成哪些影响? 这两个问题归结起来是Gradient Balancing(梯度平衡)的问题,不同任务的loss的梯度相差过大, 导致梯度小的loss在训练...
For the cross-modal prediction task, previous methods such as coupled autoencoder were limited for the prediction between two modalities since they used an alignment loss function designed between two modalities, which cannot be directly applied to this three-modality dataset. In contrast, UnitedNet ...
Loss Function Likelihood 机器学习入门之逻辑回归 逻辑回归 第一步先选择函数集 步骤一:函数集 接上一篇,我们知道,给定一个x,它属于类别 C1C_1C1的概率为 Pw,b(C1∣x)P_{w,b}(C_1|x)Pw,b(C1∣x), 如果 Pw...样的话,那么就是0。 所以在逻辑回归中,定义一个函数的好坏就通过两个类别分布的...