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...
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...
解决的主要问题:计数模型在未知场景的迁移能力(domain generalization) 与之前Crowd Counting中Domain Adaptive方法的不同:不依赖于未知场景的数据进行训练 本文的主要贡献: 我们引入了第一个domain-general人群计数框架,该框架在一个源域(source domain)上训练,可以很好地推广到任何未知的目标域。 我们设计了领域不变(DI...
RK将作为当前问题的背景知识添加到输入提示中。 This process is unsupervised as we have no manually labeled question-knowledge pairs. Besides, our model will be deployed for real scenario usage, so it also requires strong zero-shot generalization capabilities to new questions. For these two reasons, ...
In unsupervised domain adaptation (DA), 1 aims at learning from labeled source data and fully unlabeled target examples a model with a low error on the target domain. In this setting, standard generalization bounds prompt us to minimize the sum of three terms: (a) the source true risk, (...
This straightforward application enables users to find the domain and range of compositions. This method is a generalization of a technique called graphical iteration or graphical analysis, commonly used in introductions to chaos, such as in Chaos, Fractals and Dynamics. Functions with a particular ...
2.5. Domain Generalization This work is also related to domain generalization [42] in a broad sense, because of the shared goal of improv- ing performance on potentially changing target domains. A number of works have also shown that data augmenta- tion [52] during training [14, 16, 32,...
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...
DAis very important in object detection tasks, because in many industrial scenes (especially disaster accidents),image datais not only scarce, but also often too different from the data used for model training. Therefore, DA technology is needed to improve the cross-domaingeneralization performanceof...
1. Additionally, we also provide the performance of the networks trained only with augmented source data (domain generalization) as well as the oracle performance trained with target labels (super- vised learning). In all cases, the model is eva...