Domain-Driven Design领域驱动设计 Domain-Driven Design领域驱动设计

Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sen- sors, variations due to illumination, and viewpoint among others, com- puter vision applications present a very natural test bed for evaluating domain adaptation methods. In this monograph, we provide a compre- hensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, we discuss three adaptation scenarios namely, (i) unsupervised adap- tation where the “source domain” training data is partially labeled and the “target domain” test data is unlabeled, (ii) semi-supervised adaptation where the target domain also has partial labels, and (iii) multi-domain heterogeneous adaptation which studies the previous two settings with the source and/or target having more than one domain, and accounts for cases where the features used to represent the data in each domain are different. For all these topics we discuss existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. These techniques have shown improved performance on a variety of applications such as object recognition, face recognition, activity analysis, concept clas- sification, and person detection. We then conclude by analyzing the challenges posed by the realm of “big visual data”, in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability, and draw parallels with the efforts from vision community on image transformation models, and invariant descriptors so as to facilitate im- proved understanding of vision problems under uncertainty.

解决以太坊名称服务的以太网域 在浏览器中键入以太坊网站时，请查看它们。 ENS网关检索所请求的ENS域的网站，并将其呈现出来以供您查看。 ENS是使用“ .Eth” tld的以太坊的分布式加密域名系统。 Eth域名可以解析网站，或充当易于记住的Ether钱包地址。 支持语言:English

casscf-tete Casse-tête（nm）：AuQuébec，不解。 casscf-tete是一个简单程序，可为US-GAMESS CAS-SCF计算构建输入平台。 用法 用分子的电子数量以及您选择的HOMO和LUMO轨道来称呼它，它将打印出适当的$guess和$det牌组， $ ./casscf-tete 60 --homo 24 28 29 30 --lumo 31 37 34 33 --norb 270 $guess guess=moread norb=270 norder=1 $end $guess iorder(24)=27 iorder(27)=24 iorder(32)=37 iorder(37)=32 $end $det ncore=26 nact=8 nels=8 $end 安装 在Linux上，下载二进制文件 wget https://github

《Domain-Driven Design: Tackling Complexity in the Heart of Software》，中文名《领域驱动设计：软件核心复杂性应对之道》，该资源是英文原版，文字可复制。

Unsupervised Domain Adaptation by Backpropagation.pdf

目标检测，机器学习

域适应

使当前域的统一资源定位符大写 从上下文菜单中选择“大写域”，以将当前域的URL转换为大写并在弹出窗口中显示。 支持语言:English

域泛化(DG)，即分布外泛化，近年来引起了越来越多的关注。领域泛化处理一个具有挑战性的设置，其中给出了一个或几个不同但相关的领域，目标是学习一个可以泛化到看不见的测试领域的模型。