^Domain-adversarial training of neural networks. Journal of machine learning research, 17(1):2096–2030, 2016. ^ Reading digits in natural images with unsupervised feature learning. NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. ^In search of lost domain generalization. In...
In a semi-supervised scenario, the Multi-Domain Adversarial Feature Representation (mDAFR) strategy promotes the emergence of features, which are discriminative for the main learning task, while remaining largely invariant to the data sources (course from which the data was captured) in consideration...
MulANN:在Base模型的基础上,添加adversarial模块,区分不同领域学习到的表示 MMoE:每一个expert是7层全连接网络,experts的个数和领域的个数相同 Cross-Stitch:每个领域一个7层全连接expert,并且在每个hidden layer都添加线性cross-stitch单元,组合不同experts的hidden表示。 训练细节 优化器使用Adam,lr=0.001,batch siz...
In this paper we propose a hierarchical adversarial neural network (HANN) for adaptive sentiment analysis. Unlike most existing deep learning based methods, the proposed method HANN is able to share information between multiple domains bidirectionally, not just transfers information from source domain to...
Multi-AdversarialDomainAdaptation∗ZhongyiPei†,ZhangjieCao†,MingshengLong,andJianminWangKLiss,MOE;NEL-BDS;TNList;SchoolofSoftware,TsinghuaUniversity,China{peizhyi,caozhangjie14}@gmail{mingsheng,jimwang}@tsinghua.eduAbstractRecentadvancesindeepdomainadaptationrevealthatad-versariallearningcanbeembeddedintodeep...
使用对抗学习(Adversarial Training)的UDA(Unsupervised Domain Adaptation)无监督域自适应方法大部分人已经在使用了,但是本文作者发现这些方法没有考量每个域的多模态性质(the multi-modal nature of video within each domain.),即假如我使用其他模态进行协同学习时这种environmental bias会不会变小,或许一种模态下学习...
the image features should be stably detected against the differences of staining conditions among the hospitals/specimens. In this paper, we develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks in ...
【迁移学习】2018_AAAI_Multi-Adversarial Domain Adaptation论文解读,程序员大本营,技术文章内容聚合第一站。
[2] A theory of learning from different domains[3] Bridging Theory and Algorithm for Domain Adaptation[4] Unsupervised Multi-Class Domain Adaptation:Theory, Algorithms, and Practice[5] Domain-Adversarial Training of Neural Networks[6] Detecting Change in Data Streams[7] Conditional Adversarial Domain...
最后,论文还采用了对抗学习(Adversarial Learning)的方法,参考多任务学习模型[6]中的GRL[7]结构来帮助shared模块学习domain-shared的特征。 Figure 8:GRL有关结构及其整体作用过程 如图8所示,绿色部分为模型的特征抽取器,用于生成特征f,蓝色部分为常用的模型分类功能,生成最后的预测输出与对应的损失 L_y ,而红色部分...