但是这样的问题是,不同client的local centroid差异会比较大,因此采用通过加权平均的方式来修正得到local class-wise centroid: 这里的 sim是相似度函数,本文实验里用的是cosine相似度;而 全局质心 从server中得到,反映了所有clients的centroids。(类似于联邦学习全局来修正局部) 这里的加权平均结果可见,当 全局质心和原始...
Robust Federated Learning with Noisy Labelsarxiv.org/abs/2012.01700 发表于2020 这篇文章符号写得有点混乱,介绍算法的部分有点乱。。。方法上有点像用于large data mining的聚类算法,比如所有的数据分散在多块硬盘上,如何解决聚类问题。还用到了pseudo-labeling,聚类中的centroid,通过model来filter得到一个clean...
Robust Temporal Ensembling for Learning with Noisy LabelsAbel BrownBenedikt SchiffererRobert DiPietro
Robust Training for Speaker Verification against Noisy Labels 16 15:48 MTANet: Multi-band Time-frequency Attention Network for Singing Melody Extraction 1 31:15 Self-supervised Learning Representation based Accent Recognition with Persistent Accent Memory 1 00:00 Dynamic Fully-Connected Layer for Large...
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust Bo Han 1 2 Gang Niu 2 Xingrui Yu 3 Quanming Yao 4 Miao Xu 2 5 Ivor W. Tsang 3 Masashi Sugiyama 2 6 Abstract Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit every...
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels - GitHub - bupt-ai-cz/PGDF: Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
Noisy few-shot learning (NFSL) presents novel challenges primarily due to the interplay between noisy labels and limited training data. While data cleansin... X Que,Q Yu - European Conference on Computer Vision 被引量: 0发表: 2025年 Data fusing and joint training for learning with noisy lab...
machine learning and data cleaning: which serves the other? [Paper] a robust mature tomato detection in greenhouse scenes using machine learning and color analysis [Paper] privacy risks of securing machine learning models against adversarial examples [Paper] fair, robust, and data-efficient ...
Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets. To mitigate the issue, we propose a novel inference method, termed Robust Generative classifier...
现在,这个问题就成功的转化为了k-中位数分析(k-medoids problem)问题。 OK,第一部分先写到这儿,后面请看: 歪歪小白:《Coresets for Robust Training of Neural Networks against Noisy Labels》--nips2020,论文+代码分析【part2】6 赞同 · 0 评论文章...