(5)在相同的5-way 5-shot(或5-way 1-shot)评价设置下,mini和tired数据集的测试结果明显低于DomainNet和mini→CUB数据集的测试结果。这表明域间隙(由风格转换引起)与广泛使用的现实数据集(即DomainNet和mini→CUB)相比,前两个数据集的类别差距(由FSL引起的)甚至更大,这证明了使用这两个合成数据集作为新的DA-...
论文链接:[2003.08626] Domain-Adaptive Few-Shot Learning (arxiv.org) 代码:https://github.com/dingmyu/DAPN 摘要 现有的小样本学习(FSL)方法隐式假设少数target类样本与source类样本来自同一领域。然而,在实践中,这种假设通常是无效的——target类可能来自不同的领域。 本文解决了领域自适应小样本学习(DA-FSL)...
论文地址:https://www.aminer.cn/pub/5e7495c591e0111c7cee13bb/domain-adaptive-few-shot-learning 论文解读参考:https://blog.csdn.net/m0_37929824/article/details/105379668 论文代码参考:https://github.com/dingmyu/DAPN 本篇文章只记录个人阅读论文的笔记,具体翻译、代码等不展开,详细可见上述的链接. 最...
论文地址:https://www.aminer.cn/pub/5e7495c591e0111c7cee13bb/domain-adaptive-few-shot-learning 论文解读参考:https://blog.csdn.net/m0_37929824/article/details/105379668 论文代码参考:https://github.com/dingmyu/DAPN 本篇文章只记录个人阅读论文的笔记,具体翻译、代码等不展开,详细可见上述的链接. 最...
论文阅读问题总结(六):Meta-Learning with Domain Adaption for Few-shot Learning Under Domain Shift,程序员大本营,技术文章内容聚合第一站。
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications. This paper proposes an adaptive transformer network (ADAPTER), a simple but effective solution for cross-domain few-shot learning where there exist large dom...
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications. This paper proposes an adaptive transformer network (ADAPTER), a simple but effective solution for cross-domain few-shot learning where there exist large ...
Task-aware Adaptive Learning for Cross-domain Few-shot Learning Yurong Guo1, Ruoyi Du1, Yuan Dong1, Timothy Hospedales2, Yi-Zhe Song3, Zhanyu Ma1* 1Beijing University of Posts and Telecommunications, China 2University of Edinburgh, UK 3University of Surrey, UK ...
作者:凯单位:燕山大学 Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering Abstract Introduction Methodology DaFeC Framework Clustering Promotion Mechanism Overall Workflow Exp... CVPR 2019之迁移学习解读(三)Contrastive Adaptation Network for Unsupervised Domain Adaptation ...
Aug 16, 2023 25f65a3·Aug 16, 2023 History 3 Commits Repository files navigation README Few-shot-Stereo-Matching-with-High-Domain-Adaptability-Based-on-Adaptive-Recursive-Network Code for paper "Few-shot Stereo Matching with High Domain Adaptability Based on Adaptive Recursive Network" ...