To alleviate the limitation, we present a self‐supervised domain adaptation fault diagnosis network (SDAFDN),which considers two temporal dependencies to improve the transferability of the learned representations. Specifically, we first design a down‐sampling and interaction networkthat considers the ...
自监督学习(Self-supervised learning)是这两年比较热门的一个研究领域,它旨在对于无标签数据,通过设计辅助任务(Proxy tasks)来挖掘数据自身的表征特性作为监督信息,来提升模型的特征提取能力(PS:这里获取的监督信息不是指自监督学习所面对的原始任务标签,而是构造的辅助任务标签)。注意这里的两个关键词:无标签数据和辅助...
Self-Supervised Augmentation Consistency for Adapting Semantic Segmentation Nikita Araslanov, Stefan Roth 本文提出一种domain adaptation领域的分割方法,domain adaptation最经典的应用是在数据丰富的虚拟数据集上训练初始模型,然后在数据量偏小的真实数据上微调,得到一个在真实数据上表现优良的模型。比如利用游戏GTA的数...
论文阅读《Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation》 purplexu 49 人赞同了该文章 发表期刊/会议:CVPR 2021 作者单位:UC伯克利、南京大学、清华大学 动机 无监督域适应 (UDA) 将预测模型有标签的源域转移到无标签的目标域 → 而在有些场景下,源域的标签...
Thus, a novel deep neural network (DNN) named temporal self-supervised domain adaptation network (TSSDAN) is developed for machinery fault diagnosis in this article. Firstly, a temporal self-supervised learning (TSSL) method is implemented to tackle the issue of strong noise interference and ...
we propose a novel training strategy to force the tasknetwork to learn domain invariant representations in a self-supervised manner. Specif i cally, we extend self-supervisedlearning from traditional representation learning, whichworks on images from a single domain, to domain invariantrepresentation lea...
STMono3D achieves remarkable performance on all evaluated datasets and even surpasses fully supervised results on the KITTI 3D object detection dataset. To the best of our knowledge, this is the first study to explore effective UDA methods for Mono3D. 展开 ...
1. It has 3 major steps, including (i) training source model, (ii) training unsupervised domain adaptation framework, and (iii) testing the desired model. In the first step, an attention U-Net (U1) is trained using labeled CT data in the source domain in a fully-supervised manner. The...
Self-supervised learning Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer Instance-aware Self-supervised Learning for Nuclei Segmentation White Matter Tract Segmentation with Self-supervised Learning Dual-task Self-supervision for Cross-Modality Domain Adaptation Dual-Teacher...
This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly com...