[Deep learning for domain adaptation by interpolating between domains.]提出了训练联合源和目标CNN架构,但仅限于两层,因此使用更深层架构的方法显著优于[ImageNet classification with deep convolutional neural networks.],在大型辅助数据源上进行了预训练(例如: ImageNet )。 [Domain adaptive neural networks f...
To address these issues, an effective UDA method namely deep residual LSTM with Domain-invariance (DIDRLSTM) is investigated to improve the prognostic performance. First, the DRLSTM is designed as the feature extractor to learn high-level features from both source and target domains. The ...
The architecture optimizes a deep CNN for both classification loss as well as domain invariance. The model can be trained for supervised adaptation, when there is a small amount of target labels available, or unsupervised adaptation, when no target labels are available. Fig Caption: We introduce ...
作者针对这个现象,提出了如下想法:通过在如下图所示的CNN模型中加入自适应层(adaptation layer),一方面使自适应层输出的representation最小化源领域和目标领域的分布差异,另一方面最小化源领域的分类损失。 图片来源:《Deep Domain Confusion: Maximizing for Domain Invariance》 作者通过MMD来衡量源领域和目标领域的分布...
[论文笔记]Deep Domain Confusion: Maximizing for Domain Invariance 该论文提出DDC(DeepDomainConfusion)解决深度网络的自适应问题,应用于迁移学习。 摘要一般的监督学习deep-CNN模型需要在大规模的数据集上训练,但不可移植。微调预训练...应用于没有标签的目标域数据。 距离公式 其中φ(·)是由源域与目标域数据点...
In the present case the analytic or topological tool employed is Brouwer's Theorem on Invariance of Domain, which derives from his Fixed-Point Theorem. A corollary of Domain Invariance is that an injective mapping of one compact manifold to another (connected) one of the same dimension, is in...
domainconfusioninvariancedeepmaximizingadaptation DeepDomainConfusion:MaximizingforDomainInvarianceEricTzeng,JudyHoffman,NingZhangUCBerkeley,EECS&ICSI{etzeng,jhoffman,nzhang}@eecs.berkeley.eduKateSaenkoUMassLowell,CSsaenko@cs.uml.eduTrevorDarrellUCBerkeley,EECS&ICSItrevor@eecs.berkeley.eduAbstractRecentreportssuggest...
所有的层共享权重 - source domain上加入了分类损失 如果分类仅仅使用source domain上的数据进行训练,会导致过拟合,所以作者使用最小化了source domain和target domain数据上距离的特征来对分类器进行训练。 最小化MMD(作者也称为最大化domain-confusion):
Probabilistic version of the invariance of domain for contractive field and Schauder invertibility theorem are proved. As an application, the stability of probabilistic open embedding is established.doi:10.1155/S1048953302000035Ismat BegSorin GalJournal of Applied Mathematics and Stochastic Analysis...
论文信息 论文标题:Deep Domain Confusion: Maximizing for Domain Invariance论文作者:Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, Trevor Darrell论文来源:arxiv 2014