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...
[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。
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...
Domain Adaptation就是为了实现模型在另一个域中(Target Domain)的表现逼近在原域(Source Domain)的效果。这个Adaptation过程实质上就是引入某种手段,尽可能减少这两个域在特征空间的差距,也就是尽可能消除Domain shift,从而使得模型学到更普式、更Domain-invariant的特征。 上述文献综述中将DA方法主要分为 (1)One-ste...
亮点:disentanglement, shared/exclusive features 私有的encoder抽取domain-specific的特征,共享的encoder抽取的特征则是domain-invariant的。 我们使用共享encoder提取的特征进行分类。 他的训练过程还是蛮有意思的,首先为了disentanglement,我们需要private/shared encoder提取出来的特征相似度尽可能小,所谓的相似度其实就是向量...
Such methods are further limited by the scope of these features or in other words, their ability to cover the contexts or word dependencies within a sentence. In this work, we focus on reducing such dependencies and propose a domain-invariant framework for the disease name recognition task. In...
Promising empirical results indicate the strength of adver-sarial training for unsupervised domain adaptation in ASR, therebyemphasizing the ability of DANNs to learn domain invariant featuresfrom raw speech.Index Terms— Unsupervised Domain Adaptation, Raw speech,ASR, Deep Learning, CNN1. INTRODUCTION...
We suggest that explicitly modeling what is unique to each domain can improve a model's ability to extract domain-invariant features. Inspired by work on private-shared component analysis, we explicitly learn to extract image representations that are partitioned into two subspaces: one component ...
Domain invariant transfer kernel learning 发表在IEEE Trans. Knowledge and Data Engineering期刊上 深度适配网络(Deep Adaptation Network, DAN) 发表在ICML-15上:learning transferable features with deep adaptation networks 我的解读 深度联合适配网络(Joint Adaptation Network, JAN) Deep Transfer Learning with Join...