2021WACV.Domain-Adaptive Few-Shot Learning 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。
很多东西现在看不到 后面就会慢慢得到显现吧。 论文名称:《Domain-Adaptive Few-Shot Learning》 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/d...
很多东西现在看不到 后面就会慢慢得到显现吧。 论文名称:《Domain-Adaptive Few-Shot Learning》 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/d...
小样本学习(Few-shot learning)的目标是通过从基础知识中学习,最终识别具有有限支持样本的新查询。小样本学习假设基础知识和新查询样本在相同的领域中分布,虽然最近在这个设定下取得了一些进展,但对于实际应用来说这通常是不可行的。针对这个问题,本文提出解决跨领域小样本学习问题的方法,其中在目标领域中只有极少量的样...
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...
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 ...
Zhang, R., Che, T., Ghahramani, Z., Bengio, Y., Song, Y.: Metagan: An adversarial approach to few-shot learning. In: NeurIPS, pp 2371–2380 (2018) Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: A survey and new perspectives. ACM Comput. Su...
Ren H, Cai Y, Zeng Y, Ye J, Hf Leung, Li Q (2022) Aspect-opinion correlation aware and knowledge-expansion few shot cross-domain sentiment classification. IEEE Trans Affect Comput 13(4):1691–1704. https://doi.org/10.1109/TAFFC.2022.3205358 Article Google Scholar Speer R, Chin J, Hav...
现有解决方法:Transfer learning、few-shot learning、meta-learning等介绍了如何解决机器学习中的数据稀缺问题。由于每个对话域之间的差异很大,因此将信息从富资源域泛化到另一个低资源域非常困难。 本文的方法:所以本文提出了一种基于元学习(meta-learning )的领域自适应对话生成方法DAML。DAML是一个端到端可训练的对话...
Few-shot unsuper- vised image-to-image translation. arXiv e-prints, 2019. 2 [25] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In IEEE Interna- tional Conference on Computer Vision, 2015. 5 [26] Mingsheng Long, Yue Cao, Jianmin Wang...