To tackle this issue, domain adversarial neural networks (DANN) are adopted to deal with domain shift. However, if the feature extractor within DANN is cumbersome, the limited quantity of EEG data may result in
DANN目标函数 DANN目的是最小化源域分类误差项,最大化域分类误差项,但整体目标函数是最小化问题,所以在域分类误差项前加了负号,并且引入超参数λ作为权重平衡参数。 DANN的算法流程图如下: 算法步骤如下:
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...
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...
4. Domain-Adversarial Neural Networks (DANN) 我们方法的一个原始想法是将定理2所展示的思想显式地实现到神经网络分类器中。也就是说,为了学习一个可以很好地从一个域推广到另一个域的模型,我们要确保神经网络的内部表征不包含关于输入(源或目标)的判别性信息,同时对源(标记的)样例保持低风险。
Domain adversarial neural network (DANN) is a two-palyer gam: the first palyer is the domain discriminator D trained to distinguish the source domain from the target domain and the second palyer is the feature representation F trained simultaneoursly to confuse the domain discriminator D. The ...
(Mach Learn 79(1–2):151–175, 2010) and the well-known domain-aligning algorithm based on this work using Domain Adversarial Neural Networks (DANN) presented by Ganin and Lempitsky (in International conference on machine learning, pp 1180–1189). Recently, multiple variants of DANN have ...
Chapter-4 Domain-Adversarial Neural Networks (DANN) (1) Shallow DANN (single hidden layer) layers: hidden layer (feature extractor):G_f(\mathbf{x};\mathbf{W},\mathbf{b})=\text{sigmoid}(\mathbf{Wx}+\mathbf{b}) prediction layer (label prediction):G_y(G_f(\mathbf{x});\mathbf{V},...
Domain-Adversarial Neural Networks ral network and a SVM on a sentiment analysis classifica- tion benchmark. Moreover, we show that DANN can reach state-of-the-art performance by taking as input the repre- sentation learned by mSDA, confirming that minimizing domain discriminability explicit...
Their algorithm, named the domain adversarial neural network (DANN), contains a task predictor and an adversarial predictor. The task predictor was designed as an emotion classifier, while the adversarial predictor was designed as a subject classifier. These classifiers work in an adversarial manner ...