Discriminative and domain invariant subspace alignment for visual tasksUnsupervised domain adaptationGlobal adaptationLocal adaptationDistinct transformationMaximum mean discrepancyTransfer learning and domain
Domain invariant and class discriminative heterogeneous domain adaptation 2018 IEEE 3rd international conference on communication and information systems (ICCIS), IEEE (2018), pp. 227-231 Google Scholar Wang et al., 2019 Wang X., Jin Y., Long M., Wang J., Jordan M.I. Transferable normalizatio...
Adversarial Discriminative Domain Adaption 阅读笔记 文章发表于 CVPR 2017 文章利用GAN网络的思想用于cross-domain识别 文章首先提到 1 先前的生成网络不适用于大的domain shift 2 先前的辨别网络施加固定的权重,没有利用GAN的loss 1 introduction ... 查看原文 paper6:Towards Pose Invariant Face Recognition in the...
model to learn comparable cross-domain properties using a dynamic re-weighting strategy to help the model. Yang et al. (2022c) gave the idea of a framework that is easy to train and learns domain-invariant prototypes for domain adaptive semantic segmentation. In order to encourage the learning...
Embed domain adaptation into the process of learning representation: final features that are both discriminative and invariant to the change of domains Can be generalized to the semi-supervised case rather straightforwardly Domain-adversarial learning is capable beyond classification problems, e.g. descri...
featurelearningmethodarepro-posed,bothofwhichguaranteethedomaininvariantfeatureswithbetterintra-classcompactnessandinter-classseparabil-ity.Extensiveexperimentsshowthatlearningthediscrimi-nativefeaturesinthesharedfeaturespacecansignificantlyboosttheperformanceofdeepdomainadaptationmethods.IntroductionDomainadaptation,which...
Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation [TIP 2018] Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation [ECCV2018] Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation [CVPR2018] Unsupervised ...
In this paper, we observe that modeling the image distributions is not strictly necessary to achieve domain adaptation, as long as the latent feature space is domain invariant, and propose a discriminative approach. 3. Generalized adversarial adaptation We present a general framework for adversarial ...
By optimizing an objective that simultaneously minimizes classification error and maximizes domain confusion (right), we can learn representations that are discriminative and domain invariant. 3.3 MMD MMD metric helps decide where in the network to place the adaptation layer and the dimension of the ...
distributions between the domains, thus obtaining a domain-invariant representation. Various methods have been proposed to achieve this, including the domain antagonisticneural network(DANN), which employs antagonistic training to align the source and target feature distributions at either a functional or...