Domain-adversarial learning is used to train a domain-invariant 2D U-Net using labelled and unlabelled data. This approach is evaluated on both seen and unseen domains from the M&Ms challenge dataset and the domain-adversarial approach shows improved performance as compared to standard training. ...
^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...
Second, via adversarial learning, we make the distributions of latent variables being indistinguishable between the reconstruction process of the face photograph and that of the face sketch. Finally, given an input face photograph, the latent variable obtained by reconstructing this face photograph is ...
Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning笔记 wastelands 珂学家摘要 首先在图像级和语义级恢复的损失函数下分别综合研究了两种用于对抗性鲁棒性增强的像素去噪方法(即现有的基于加法和未探索的基于过滤的方法),表明与现有的基于像素的基于加法的方法相比,逐像素滤波可以...
Multi-AdversarialDomainAdaptation∗ZhongyiPei†,ZhangjieCao†,MingshengLong,andJianminWangKLiss,MOE;NEL-BDS;TNList;SchoolofSoftware,TsinghuaUniversity,China{peizhyi,caozhangjie14}@gmail{mingsheng,jimwang}@tsinghua.eduAbstractRecentadvancesindeepdomainadaptationrevealthatad-versariallearningcanbeembeddedintodeep...
Recently, adversarial learning has been successfully em- bedded into deep networks to learn transferable features to reduce distribution discrepancy between the source and tar- get domains. Domain adversarial adaptation methods (Ganin and Lempitsky 2015; Tzeng et al. 2015) are among the top- ...
Progressive Adversarial Learning for Multi-target Domain Adaptation Unsupervised domain adaptation addresses the problem that model trained on labeled source domains can be transferred to unlabeled target domains, which cru... Q Tian,Z Lu,J Zhou - 《Neural Processing Letters》 被引量: 0发表: 2023年...
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...
Learning Domain-Robust Text Representations using Adversarial Training Trevor CohnTrevor CohnTimothy BaldwinYitong Li Jan 2018 Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (in domain) and ...
3Yang, Y., Hospedales, T.M.: A Unified Perspective on Multi-domain and Multi- task Learning. ICLR (2015)... Y Yang,TM Hospedales 被引量: 1发表: 2017年 Unifying Multi-domain Multitask Learning: Tensor and Neural Network Perspectives In G. Csurka, editor, Domain Adaptation in ...