Deep Transfer Network(这里简称DTN)就是一个用深度网络去做Domain adaptation的理念,这个网络被分为了两种类型的层,共享特征抽取层和判别层。第一层共享特征抽取层用于匹配边缘分布,共享特征抽取层可以是一个多层感知机,如果网络层数为l的话,我们一般会把前l-1层看作共享特征抽取层,而l-1层的输出则是一种分布相...
In this paper, we propose a new domain adaptation framework named Deep Transfer Network (DTN), where the highly flexible deep neural networks are used to implement such a distribution matching process. This is achieved by two types of layers in DTN: the shared feature extraction layers which ...
Deep transfer network: Unsupervised domain adaptation 这篇文章提出了DTN方法。源域有label,目标域无label,设计伪标签。不同之处在于,假设条件分布P(y^s|x^s)、P(y^t|x^t)是不一样的。做法是先用一些基础的分类器,在源域上训练好,再给目标域打上pseudo-label伪标签。利用源域和目标域的数据和标签和伪...
在[29]中,两个不同的网络对未标记的样本分配伪标签,另一个网络利用样本进行训练,得到目标识别表示。deep transfer network (DTN)使用支持向量机(SVMs)和MLPs等基本分类器获取目标样本的伪标签,估计目标样本的条件分布,并将边缘分布和条件分布与MMD准则进行匹配。[32]在将分类器自适应转换到残差学习框架时,使用伪标...
deep transfer network (DTN)使用支持向量机(SVMs)和MLPs等基本分类器获取目标样本的伪标签,估计目标样本的条件分布,并将边缘分布和条件分布与MMD准则进行匹配。[32]在将分类器自适应转换到残差学习框架时,使用伪标签构建条件熵,保证目标分类器f t很好地适应目标特有的结构。 统计标准 尽管一些基于离散的方法搜索伪...
A novel Deep Tree Network (DTN) is proposed to partition the spoof sam- ples into semantic sub-groups in an unsupervised fashion. When a data sample arrives, being know or unknown at- tacks, DTN routes it to the most similar spoof cluster, and makes the binary decision. In a...
18 Nov 2019·Ruosi Wan,Haoyi Xiong,Xingjian Li,Zhanxing Zhu,Jun Huan· Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer learning can us...
作者又提出来一个DTN(Domain Transfer Network)方法,如果有印象,在基于统计差异重构中的Deep Transfer Network缩写重名有木有!用的数据集也都是图片,比如源域 S 是真实风格的图片,目标域 T 是卡通风格的图片,想做的任务是将真实风格的图片转化成卡通风格的,见下图。 其实用的也是GAN的思想,和CycleGAN有点类似,作...
Deep transfer network: Unsupervised domain adaptation 以上方法都假设源域和目标域条件分布是一样的,边缘分布不一样。这篇文章在上一篇基于label迁移知识中也提到了,提出了DTN方法。源域有label,目标域无label,设计伪标签。不同之处在于,假设条件分布P(y^s|x^s)、P(y^t|x^t)是不一样的。做法是先用一些...
[33] presented an unsupervised deep tree network (DTN) to solve the problem. Comparing with previous learning methods, their performance is greatly improved. But they also focus on revising the architectures of CNN and use complex models. In this paper, we also present a CNN-based method but...