Learning from noisy labels [1], [2], [3], [4], [5], [6] for deep models is a challenging problem in practice. Since data noise is ubiquitous in the real world, collecting training data with clean labels would be resource-intensive, especially for some domains with ambiguous labels, ...
全局模型如何更新? Client local质心如何算 local质心如果直接计算,会受到样本噪声的影响,因此需要做样本筛选 按理说 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se...
去杭州之前就想专门整理一下在有噪声样本下的分类损失,在这之前,先就较经典的一篇分析一下各种推导原理,剩下就简单一点只看损失部分～ 背景在分类任务上,最普遍的损失函数是 Cross Entropy,即交叉熵损失: 该…
Finally, the training data with diluted labels are further used to train a robust classifier. Empirically, we conduct extensive experiments on both synthetic and real-world noisy datasets by comparing our LEND with several representative robust learning approaches. The results verify the effectiveness ...
Symmetric Cross Entropy for Robust Learning with Noisy Labels 笔记,程序员大本营,技术文章内容聚合第一站。
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...
Official Repo: https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels Reproduce result for ICCV2019 paper "Symmetric Cross Entropy for Robust Learning with Noisy Labels" Update In the tensorflow version Official Repo, the model uses l2 weight decay of 0.01 on model.fc1, which ...
Learning with Noisy Labels for Robust Point Cloud Segmentation (ICCV2021 Oral) - pleaseconnectwifi/PNAL
Learning Adaptive Loss for Robust Learning with Noisy Labels Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current robust loss functions, however, inevitably involve hyperparameter(s) to be tuned, manually or heuristically through cross validation,...
Learning with noisy labelsDialog state tracking (DST), which estimates dialog states given a dialog context, is a core component in task-oriented dialog systems. Existing data-driven methods usually extract features automatically through deep learning. However, most of these models have limitations. ...