标题:Causality Inspired Representation Learning for Domain Generalization 会议:CVPR 统计学上的相关(stastistical dependence)不一定表示因果关系。CIRL 旨在挖掘内在的因果机制(intrinsic causal mechanism)。 名词解释: DG(Domain Generalization)域泛化 SCM(Structural Causal Model)结构化因果模型 因果图 输入X由两部分...
因此,我们提出基于因果因素的属性来学习因果表征,作为一种模拟,同时继承其优越的泛化能力。 3.1 从因果视角看领域泛化 领域泛化的主流研究集中在建模观测输入和相应标签之间的统计依赖关系,即 P(X, Y),这通常假设在不同域之间是变化的。为了获得不变的依赖关系,主流方法通常通过边缘或条件上强制分布不变性来减少跨域...
本论文入选了CVPR 2022会议,并获得了Oral。ReadPaper邀请了作者吕芳蕊入驻并回答论文十问,在详细阅读论文之前不妨先速读十问做个初步了解。 论文阅读链接: Causality Inspired Representation Learning for Domain Generalizationreadpaper.com/paper/4606769730388238337 论文摘要: 领域泛化本质上是一个分布外泛化问题,旨在将...
论文题目:Causality Inspired Representation Learning for Domain Generalization 作者列表:吕芳蕊,梁健,李爽(北京理工大学,通讯作者),臧斌,刘驰,王子腾,刘迪 论文摘要: 领域泛化本质上是一个分布外泛化问题,旨在将从多个源领域学习到的知识泛化到不可见的目标领域上。现有的主流方法利用统计模型来建模数据和标签之间的依赖...
Depending on the nature of such information, the learning problem may turn into domain adaptation (DA), domain generalization (DG), leaning using ... S Motiian - West Virginia University. 被引量: 0发表: 2019年 加载更多研究点推荐 Causality Inspired Representation Learning Domain generalization Repr...
文章提出了Causality Inspired Model Interpreter (CIMI),一种基于因果推理的解释器。 在XAI中 变量集合(a set of variables)可以作为模型预测的可能原因(possible causes),如果其满足因果充分性假设(causal sufficiency assumption)。 核心问题:如何有效地 发现突出的共同原因(prominent common causes),并能从大量特征和...
This repo provides a demo for the CVPR 2022 paper "Causality Inspired Representation Learning for Domain Generalization" on the PACS dataset. Requirements Python 3.6 Pytorch 1.1.0 Training from scratch Please first download the PACS dataset from http://www.eecs.qmul.ac.uk/~dl307/project_iccv2017...
Logic-inspired Deep Neural Networks. Le, Minh. arXiv:1911.08635 A Novel Neural Network Structure Constructed according to Logical Relations. Wang, Gang. arXiv:1903.02683 Augmenting Neural Networks with First-order Logic. Li, Tao & Srikumar, Vivek. ACL 2019[code] ...
1-9.R. Burke and B. Blumberg. Using an ethologically-inspired model to learn apparent temporal causality for planning in synthetic creatures. In First International Joint Conference on Autonomous Agents and Multiagent Systems , pages 326–333, 2002....
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