《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-Distribution Detection on Graphs》(NeurIPS 2022) GitHub: github.com/Emiyalzn/GraphDE [fig9]...
Few-shot Learning with Noisy Labels — Supplemental Material — Kevin J Liang1 Samrudhdhi B. Rangrej2 Vladan Petrovic1 1Facebook AI Research 2McGill University kevinjliang@fb.com Tal Hassner1 We include supplemental material for our work here. Sec. A shows the mislabeled samples ...
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...
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...
Learning设定，例如在远程监督（Distant Supervision）的噪音训练数据上如何实现Few-Shot Learning：Tianyu ...
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 ...
Few-shot learning is seeking to generalize well to unseen tasks with insufficient labeled samples. Existing works have achieved generalization by exploring inter-class discrimination. However, their performance is limited because sample discrimination is neglected. In this work, we propose a metric-based...
论文阅读笔记《Learning to Compare: Relation Network for Few-Shot Learning》,程序员大本营,技术文章内容聚合第一站。
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...
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...