To this end, we introduce domain adversarial training into entropy minimization. Furthermore, we consider the misalignment caused by domain adversarial training under severe label shift. Therefore, we propose method called entropy minimization and domain adversarial training guided bylabel distribution ...
1、ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation 目的:这篇文章和前几篇一样的思想,都是用对抗学习的思想来做域自适应学习分割,一个系列的文章,前面几篇可以当做了解,这篇着重学习代码,以及熵最小化这种思想。从网络,训练过程多方面学习,尤其把网络训练起来。 Adversar.....
Adversarial training for UDA is the most explored approach for semantic segmentation. It involves two networks.One network predicts the segmentation maps for the input image, which could be from source or target domain, while another network acts as a discriminator which takes the feature maps from...
1. We explore the two paradigms of correlation alignment and entropy minimization, by formally demonstrating that, at its optimum, correlation alignment attains the minimum of the sum of cross-entropy on the source domain and of the entropy on the target. 2. Motivated by the urgency of penalizi...
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation Tuan-Hung Vu,Himalaya Jain,Maxime Bucher,Matthieu Cord,Patrick Pérez valeo.ai, France IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Oral) ...
域自适应学习分割:ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation 定义为: 因此,熵损失定义为所有像素的熵的和: 训练时,结合源域上的有监督损失,联合优化,分割网络总损失函数为: 对于上面公式,也可以从自监督的角度进行理解,这相当于soft-assignment...结果,只要它和类别先...
In this work, we present a novel unsupervised domain adaptation framework, calledBoundary and Entropy-driven Adversarial Learning (BEAL), to improve the accuracy to segment the OD and OC over different fundus image datasets. Our method is based on two main observations. First, deep networks traine...
In this work, we innovatively employ a fine-grained unsupervised domain adaptation semantic segmentation method with increased entropy certainty, and guide the model for finer-grained feature alignment by adversarial learning, while increasing the pixel certainty near the category boundaries. Our approach ...
Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 2517–2526. [Google Scholar] Nie, D.; Gao, Y.; Wang, L.; Shen, D. ...
Work-in-progress: Sequence-crafter: Side-channel entropy minimization to thwart timing-based side-channel attacks. In Proceedings of the International Conference on Compliers, Architectures and Synthesis for Embedded Systems (CASES), New York, NY, USA, 13–18 October 2019. [Google Scholar] Köpf...