Symmetric Cross Entropy for Robust Learning with Noisy Labels https://
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...
Besides, modeling the rectifying vector as a latent variable and learning the meta-network can be seamlessly integrated into the SGD optimization of the classification network. We evaluate WarPI on four benchmarks of robust learning with noisy labels and achieve the new state-of-the-art under ...
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 ...
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...
Learning with Noisy Labels for Robust Point Cloud Segmentation (ICCV2021 Oral) - pleaseconnectwifi/PNAL
Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples. We propose Rank Pruning (RP) to solve noisy PN learning and the open problem of estimating the noise...
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. ...
In this paper, we present RCL-Net, a simple yet effective robust consistency learning network, which combats label noise by learning robust representations and robust losses. RCL-Net can efficiently tackle facial samples with noisy labels commonly found in real-world datasets. Specifically, we first...