Deep learning algorithms have shown significant results in the intra and cross database. This paper used deep learning for extracting the inclusive and favorable features of the person from the face. The extracted features are used for classifying the face image as a real face or genuine face. ...
Domain Generalization with Adversarial Feature Learning 会议:IEEE CVPR_Computer Vision and Pattern Recognition(CCF-A类) 方法:MMD-AAE(MMD-based Adversarial autoencoders基于最大均值差异的对抗自编码器) 创新点:基于最大均值差异和对抗自动编码机来进行领域泛化,同时将MMD计算扩展到多领域计算。 自编码器,通常由...
Representation learning vs Deep Metric Learning 似乎这两者之间的区别并不是很大,所以有的学者直接就称为:Metric and Representation Learning,并给出了如下的描述。 很多machine learning or data mining methods需要精确的representations去构建有效的classification, regression 和 clustering 模型,因此,Representation learnin...
Feature representation(特征表达) 和 metric learning (度量学习) 是 person re-Identification models 中两个关键的部分。本文关注在 feture representation 并指出 hand-crafted histogram features 与CNN features 是互补的。我们提出一个特征提取网络Feature Fusion Net(FFN)来表达行人图像。在FFN中,反向传播使得CNN f...
In this chapter, we show that deep neural networks jointly learn the feature representation and the classifier. Through many layers of nonlinear processing, DNNs transform the raw input feature to a more invariant and discriminative representation that can be better classified by the log-linear model...
卷积神经网络具有表征学习(representation learning)能力,能够按其阶层结构对输入信息进行平移不变分类(shift-invariant classification),因此也被称为“平移不变人工神经网络。 1)卷积:对图像元素的矩阵变换,是提取图像特征的方法,多种卷积核可以提取多种特征。一个卷积核覆盖的原始图像的范围叫做感受野(权值共享)。一次...
>Feature Representation Learning with MLP Wide&Deep learning.是一个生成模型.Wide learning对应单层的感知机,通过获取直接的历史信息来获取"memorization";Deep learning对应的是多层感知机,通过抽象以及深层次的特征表示来获取"generalization".部署这个模型需要进行特征工程,选择好的特征来获取其"memorization"以及"general...
Deep Low-Rank Coding for Transfer Learning Recent researches on transfer learning exploit deep structures for discriminative feature representation to tackle cross-domain disparity. However, few of them are able to joint feature learning and knowledge transfer in a unified deep f... Z Ding,S Ming,...
This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be...
Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks...