这个形式就和ridge regression很相似了,这也区分于传统的字典稀疏学习,因为这里只有一个优化变量 Ds target domain: {Dt∗,Yt∗}=argminDt,Yt||Xs−DtYt||F2+λ2||Yt||1 (3) s.t. ∀i ||di||22≤1 这里就是标准的字典稀疏学习的形式了,但是再观察这个式子,对于两个优化变量,仅仅是促使了...
The goal of unsupervised domain adaptation (UDA) is to eliminate the cross-domain discrepancy in probability distributions without the availability of labeled target samples during training. Even...doi:10.1007/978-3-319-75786-5_17Guangbin Wu
Unsupervised domain adaptation for regression using dictionary learning Domain adaptation in machine learning and image processing aims to benefit from gained knowledge of the multiple labeled training sets (i.e. source domain)... M Dhaini,M Bérar,P Honeine,... - 《Knowl Based Syst》 被引量:...
Supervised learning · regression problem · classification problem Unsupervised learning · clustering algorithm Re-ID 2019 Review accuracy 挖掘 区别性大的线索 提高 unsupervised domain adaptation accuracy. Video re-id: frame-level weights are... help improve the generalization performance of re-ID ...
* 题目: A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression* PDF: arxiv.org/abs/2309.1481* 作者: Kyriaki-Margarita Bintsi,Tamara T. Mueller,Sophie Starck,Vasileios Baltatzis,Alexander Hammers,Daniel Rueckert* 其他: Accepted at GRAIL, ...
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation Suman Saha* ETH Zurich Anton Obukhov* Danda Pani Paudel Menelaos Kanakis ETH Zurich ETH Zurich ETH Zurich Stamatios Georgoulis ETH Zurich Luc Van Gool ETH Zurich, KU Leuven Yuhua Chen ETH Zur...
We consider the problem of unsupervised domain adaptation from multiple sources in a regression setting. We propose in this work an original method to take benefit of different sources using a weighted combination of the sources. For this purpose, we define a new measure of similarity between ...
Domain adaptationAdversarialMultiple sourcesDiscrepancyWe consider the problem of unsupervised domain adaptation from multiple sources in a regression setting. We propose in this work an original method to take benefit of different sources using a weighted combination of the sources. For this purpose, we...
we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain,and training is supplemented with different unlabelled datasets from the target real domain.We approach the problem of 3D reconstruction using volumetric regression and...
However, by analyzing the features of the anchor-free one-stage detector, in this paper, we find that negative transfer may occur because the feature distribution varies depending on the regression value for the offset to the bounding box as well as the category. To obtain domain invariance by...