当不使用 DANN 训练过程时(即仅使用 AskUbuntu 论坛数据进行训练,然后在 Android 数据上测试模型),曲线下面积 (AUC) 为 0.61。当使用DANN框架及其训练过程时(训练中使用AskUbuntu数据输入和标签以及Android数据输入;不使用Android标签),AUC增加到0.69。当 Android 论坛数据中的少量标签被添加到 DANN 训练过程中时,AUC...
DANN的目的 通过对抗的方式可以提取domian无关的特征,从而实现domain adaption。这就是DANN(Domain-Adversarial Neural Networks)。 输入分为Source Domain和Target Domain Source Domain:是原始训练数据,有标签 Target Domain: 是测试数据,没有标签 希望在提取特征后,source和target分不出差异。 DANN模型框架 这个模型...
对于DANN算法,自适应参数λ在10−2和1之间的9个对数范围内选择。隐藏层的大小 是50或100。最后,学习率μ固定在10−3。 对于NN算法,我们使用与上面的DANN完全相同的超参数和训练过程,只是我们不需要自适应参数。注意,我们可以使用λ = 0的DANN实现(算法1)来训练NN。 对于SVM算法,超参数C是从10−5和1之间...
[ Domain-Adversarial Neural Networks(DANN)] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francois Laviolette, Mario Marchand, and Victor Lempitsky.Domain-adversarial training of neural networks.Journal of Machine Learning Research, 17(59):1?5, 2016. PS: 这篇hi...
领域对抗神经网络 (Domain Adversarial Neural Network,DANN)[2]是域自适应使用最为广泛的方法之一。 它的核心想法就是在表示层面减少边缘分布P(X)和Q(X)的差异。 生成对抗网络Generative Adversarial Net (GAN)[3]引入了一个判别器来刻画真实的数据分布和生成的数据分布之间的差异。受到GAN的启发,DANN使用域判别器...
(2010) and the well-known domain-aligning algorithm based on this work using Domain Adversarial Neural Networks (DANN) presented by Ganin and Lempitsky (2015). Recently, multiple variants of DANN have been proposed for the related problem of domain generalization, but without much discussion of ...
Domain-Adversarial Training of Neural Networks in Tensorflow "Unsupervised Domain Adaptation by Backpropagation" introduced a simple and effective method for accomplishing domain adaptation with SGD with a gradient reversal layer. This work was elaborated and extended in "Domain-Adversarial Training of Neur...
Domain Adversarial Neural Networks for Dysarthric Speech Recognition Speech recognition systems have improved dramatically over the last few years, however, their performance is significantly degraded for the cases of accented or impaired speech. This work explores domain adversarial neural networks (DANN....
The domain adversarial neural networks (DANN), where the classification loss and domain loss jointly update the parameters of feature extractor, are adopted to deal with the domain shift. However, limited EEG data quantity and strong individual difference are challenges for the DANN with cumbersome ...
针对上述问题,本文通过结合域对抗训练网络(Domain-Adversarial Training of Neural Networks,DANN)和语音增强方法,提出了一种基于DANN的卷积语音增强优化算法,称为 ... 杨大为 - 《西安电子科技大学》 被引量: 0发表: 2022年 An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Training ...