Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics Zhixing Zhong, Junchen Hou, Zhixian Yao, Lei Dong, Feng Liu, Junqiu Yue, Tiantian Wu, Junhua Zheng, Gaoliang Ouyang, Chaoyong Yang & Jia Song Nature Communications volume 15, Ar...
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIsElectrical Engineering and Systems Science - Image and Video ProcessingLeft atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the ...
of performance between tasks within individuals. More broadly, any general theory of learning that aims to describe a range of phenomena through a specific set of computational principles has to offer a theoretical account of how and why transfer, discrimination, and generalization take place, or ...
Learning to Learn Single Domain Generalization Fengchun Qiao University of Delaware fengchun@udel.edu Long Zhao Rutgers University lz311@cs.rutgers.edu Xi Peng University of Delaware xipeng@udel.edu Abstract We are concerned with a worst-case scenario in model generalization, in the sense that a ...
Domain adaptationis simply the procedure to learn a representation or model for the source domain and evaluating it on the target domain. Typically in initial unsupervised approaches[406], the labels for source domain were utilized for achieving generalization on the target domain with incomplete or ...
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization 论文源码:https://github.com/YBZh/EFDM 1. Introduction 传统的特征分布匹配方法通常假定特征遵循高斯分布,通过匹配特征的均值和标准差来实现。然而,现实世界中的数据特征分布通常较复杂,不能简单地用高斯分布来建模,因此仅...
Although these constant actions are less general than parameterized actions (e.g., copying a value from one field to another), they still support generalization in the conditions. Thus, in the worst case the Decision Tree is at least as general as Example-Tracing, with learned models typically...
Domain adaptation and generalization Deep learning models often fail to achieve robust segmentation in a different domain, making it difficult to be deployed in a wide variety of clinical settings. This is particularly true for studies that require highly-specialized labeled data that is only available...
Towards Principled Disentanglement for Domain Generalization Hanlin Zhang1,* Yi-Fan Zhang2,* Weiyang Liu3,4 Adrian Weller3,5 Bernhard Scho¨lkopf4 Eric P. Xing1,6 1Carnegie Mellon University 2Chinese Academy of Science 3University of Cambridge 4Max Planck Institute for Intel...
In the multi-source domain generalization setup, we have access to M similar but distinct source domains, S={Si}i=1M. In general, we assume that the joint distribution of each domain PXY(i) is different from that of others, PXY(i)≠PXY(i′) when i≠i′. Each source domain ...