To address this, we propose a more nuanced view of domain-invariant representations by introducing the concept of Domain-Orthogonal Invariant (DOI) information. As illustrated in Fig. 1, the shared information (blue part) among different domains within the same category is emphasized, while domain...
Domain-invariant representationsFace synthesismulti-domainmulti-level featuresCross-domain face synthesis plays a positive role in the real world. It is challenging to synthesize high-quality faces across multiple domains based on limited paired data because the multiple mappings between different domains ...
论文翻译:Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation 摘要 监督学习的成功依赖于假设训练和测试数据来自相同的潜在分布,这在实践中往往是无效的,因为潜在的分布转移。鉴于此,现有的非监督域自适应方法大多集中于实现域不变表示和小源域误差。然而,最近的研究表明,这并不足以...
{werner.zellinger, edwin.lughofer, susanne.saminger-platz}@jku.atThomas Grubinger & Thomas Natschläger †Data Analysis SystemsSoftware Competence Center Hagenberg, Austria{thomas.grubinger, thomas.natschlaeger}@scch.atA BSTRACTThe learning of domain-invariant representations in the context of ...
4.1 Learning Domain-invariant Representations ①深度伪造连续学习中,新任务样本的数量通常较少,不能很好地代表新任务数据的分布。因此,我们基于有监督对比学习对齐新旧任务之间的特征,这样有利于新任务的学习和原有知识的保存。 Cross-Entropy Loss. ①学生模型可以通过交叉熵损失( )直接从任务标签中学习,从而分离出真...
In this paper, we propose DI-V2X, that aims to learn Domain-Invariant representations through a new distillation framework to mitigate the domain discrepancy in the context of V2X 3D object detection. DI-V2X comprises three essential components: a domain-mixing instance augmentation (DMA) module, ...
Unlike the prior works that focus on learn- ing domain-invariant representations of instances by using domain adversarial training [15, 32, 42, 8, 30], our method constructs the transferrable factors across different domains explicitly. Our model includes two ...
This repository contains code for reproducing the experiments reported in the paperCentral Moment Discrepancy (CMD) for Domain-Invariant Representation Learningpublished at the International Conference on Learning Representations (ICLR2017) by Werner Zellinger, Edwin Lughofer and Susanne Saminger-Platz from...
所以目前大多数UDA(unsupervised domain adaptation)关注domain-invariant representations 和 source domain error,但是最近的工作表明这两点并不能保证model在target domain具有很好的泛化性。 同时在实际应用过程中,虽然target domain的labeled data很难收集,但是可以用有限的label data来帮助model training,这是非常有效的 ...
Person Re-Identification by Deep Learning Multi-Scale Representations 论文笔记 一、提出问题 现有的re-id方法主要依赖于单一尺度的外观信息,但是这不仅会忽略了其他不同尺度的潜在有用的显性信息,而且还失去了跨尺度挖掘隐式相关互补的优势。 在不同尺度上的特征学习可能不同甚至相互不一致,因此多尺度的直接特征连接...