Gong, "Large- scale domain adaptation via teacher-student learning," in In- terspeech, Stockholm, Sweden, Aug 2017, pp. 2386-2390.J. Li, M. L. Seltzer, X. Wang, R. Zhao, and Y. Gong, "Large- scale domain adaptation via teacher-student learning," Proc. In- terspeech, 2017....
The teacher-student (T/S) learning has been shown effective in unsupervised domain adaptation \cite{ts_adapt}. It is a form of transfer learning, not in terms of the transfer of recognition decisions, but the knowledge of posteriori probabilities in the source domain as evaluate...
不同的是,蒸馏学习往往是固定一个teacher网络,student网络规模要小于teacher,对比学习中,两个网络结构常常一致,并且是共同更新网络参数,蒸馏学习中teacher网络的参数固定。当然还有输入、loss、参数更新等不同,但蒸馏网络提供给了我们理解对比学习架构的另一种思考方式。在对比学习中常用的momentum update的更新方法和stop ...
其次,在Clipart1k数据集上,我们的模型是唯一一个超越oracle模型的模型,说明Mean Teacher +对抗性学习所采用的相互学习能够弥补领域差距。在Watercolor2k上进行的实验中可以发现类似的观察结果。 Adverse Weather Adaptation.设置结果:正常天气对不利天气的适应见表3。我们还报告了oracle模型(完全监督)和每个竞争对手之间的...
Compared with domain adaptation methods, our method also has the best F1 score (0.8053) from STARE to DRIVE and a competitive F1 score (0.8001) from DRIVE to STARE. 展开 关键词: Retinal vessel segmentation Domain adaptation Teacher–student network Full-resolution ...
无监督域自适应(Unsupervised domain adaptation,UDA)的目的是将从标记源域学习到的知识转移到不同的未标记目标域。 UDA 方法: ① Domain-level UDA ,通过将源域和目标域在不同尺度水平上进入相同的分布来缓解源域之间的分布差异; ② fine-grained category-level UDA,通过将目标样本推向每个类别中的源样本的分布...
This paper focuses on domain adaptation techniques for real-world vision systems, particularly for the YOLO family of single-shot detectors known for their fast baselines and practical applications. Our proposed SFDA method - Source-Free YOLO (SF-YOLO) - relies on a teacher-student framework in ...
Teacher-student framework for cross-domain adaptation 教师-学生框架进行跨领域适配 MICCAI'24 MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection [arxiv] Adapting clip for few-shot medical image anomaly detection 对CLIP模型进行适配,以用于少样本图片异常检测1...
(segmentation)43,44. Furthermore, combinations of these concepts have been proposed, e.g. by Chen et al.45. Techniques, that have been originally introduced for semi-supervised learning (SSL), have also been investigated in the context of unsupervised DA. For example, teacher–student models ...
This paper focuses on domain adaptation techniques for real-world vision systems, particularly for the YOLO family of single-shot detectors known for their fast baselines and practical applications. Our proposed SFDA method - Source-Free YOLO (SF-YOLO) - relies on a teacher-student framework in ...