域内不变特征(internally-invariant features),与分类有关的特征,产生于域的内部,不受其他域的影响,主要抓取数据的内在语义信息; 域间不变特征(mutually-invariant features),跨域迁移知识,通过多个域产生,共同学习的一些知识;本文认为,把这两种特征有效充分地结合起来,可以得到泛化性更好的模型。注意我们的方法类似...
将源域和目标域特征分为domain invariant和domain specify。domain invariant为两个域共有特征,而domain specify为私有。 2.简介 这篇论文提出了一个新的无监督领域自适应的方法——协作对抗网络(Collaborative and Adversarial Network,CAN),这个方法是通过网络的域协作和域对抗完训练完成的。 该网络先通过collaborative ...
[5] Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition [6] wnhsu/FactorizedHierarchicalVAE [7] Unsupervised Speech Recognition 文中图片是我按着论文内容画的示意图,如有错误,还请不吝纠正。
learning domain-invariant features in an adversarial manner. 不得不说,作者的这个方法跟我之前做图像分类域适应的想法不谋而合,也是寻求一个 中间域,利用中间域更小的域差逐渐适应到目标域,我感觉这种思路的理论来源于更早的一篇论文:Curriculum Learning,论文的主旨是讲:模型的学习和人类学习一样遵循一个由难到...
Unsupervised domain adaptation (UDA) mainly explores how to learn domain-invariant features from the source domain when the target domain label is unknown. To learn domain-invariant features requires aligning the distribution of samples from two domains in the feature space, which can be achieved by...
另一个用于无监督域自适应的是特征自适应,其目的是使用CNN提取域不变特征(domain invariant features),而不考虑输入域之间的外观差异。大多数方法在对抗性学习场景中区分源/目标域的特征分布(Ganin等人,2016;Tzeng等人,2017年;Dou等人,2018年)。此外,考虑到平面特征空间的高维性(high-dimensions of plain feature sp...
Unsupervised domain adaptation (UDA) mainly explores how to learn domain-invariant features from the source domain when the target domain label is unknown. To learn domain-invariant features requires aligning the distribution of samples from two domains in the feature space, which can be achieved by...
Domain Generalization via Model-Agnostic Learning of Semantic Features 论文链接:https://proceedings.neurips.cc/paper/2019/hash/2974788b53f73e7950e8aa49f3a306db-Abstract.html 以往DG 中都是实现特征空间对齐,目的是 domain invariant。本文还进行了另一种对齐:语义空间对齐,目的是保持多个源域在语义空间上 cl...
1.Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned...
Our method also shares some similarity with representation disentanglement such as DADA [29] where a feature is disentangled into domain-invariant, domain-specific and class-irrelevant features. Different from our method, DADA disentangles the feature with additional disentangler network and uses auto-...