几篇论文实现代码:《Few-shot learning with noisy labels》(CVPR 2022) GitHub: github.com/facebookresearch/noisy_few_shot 《GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribut...
While humans are generally able to separate the noisy samples from the clean samples with some scrutiny, this is in large part due to prior conceptual understandings of the classes depicted. Few-shot models presented with support sets such as those in Fig. 1 are tasked with learni...
When Source-Free Domain Adaptation Meets Learning with Noisy Labels ICLR 2023 University of Western Ontario arxiv.org/pdf/2301.1338 最近的无源域自适应(source-free domain adaptation, SFDA)方法专注于在特征空间中学习有意义的聚类结构,成功地在不访问私有源数据的情况下将源域知识自适应到无标记的目标域。然...
Few-shot Learning with Noisy Labels Authors: Kevin J Liang, Samrudhdhi B. Rangrej, Vladan Petrovic, Tal Hassner This repository is the official PyTorch implementation of the CVPR 2022 paper Few-shot Learning with Noisy Labels. Citation If you find any part of our paper or this codebase use...
Very few researches of few-shot learning (FSL) have been done in HAR to address the above problem , though FSL has been widely used in computer vision tasks. Besides, it is impractical to annotate sensor data with accurate activity labels in real-life applications. The noisy labels have ...
从已有方法可以看出，NLP解决Few-Shot Learning问题的有效方法就是，引入大规模外部知识或数据，因此无标注...
In this study, we present an approach to neuropharmacological research by integrating few-shot meta-learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach fa...
33. Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions 会议:CVPR 2020. 作者:Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha 链接:https://openaccess.thecvf.com/content_CVPR_2020/papers/Ye_Few-Shot_Learning_via_Embedding_Adaptation_With_Set-to-Set_Functions_CVPR_2020_paper...
We gratefully acknowledge the support of NVIDIA Corporation, United States with the donation of the A100 80 GB GPU used for this research under the NVIDIA Academic Hardware Grant Program.References (47) LimJ.Y. et al. SCL: Self-supervised contrastive learning for few-shot image classification ...
Learning from limited exemplars (few-shot learning) is a fundamental,unsolved problem that has been laboriously explored in the machine learningcommunity. However, current few-shot learners are mostly supervised and relyheavily on a large amount of labeled examples. Unsupervised learning is a more...