noise label 的本质不仅仅是过拟合 noise label下ce的弱点 对称ce是怎么做的 RCE为什么能抗噪 SCE是怎么增强hard sample的拟合效果的 Robust Loss Functions under Label Noise for Deep Neural Networks Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels ...
论文:2017 AAAI | Robust Loss Functions under Label Noise for Deep Neural Networks 作者:Ghosh, Aritra, Himanshu Kumar和P. Shanti Sastry 机构:Microsoft, Indian Institute of Science 引用量:493 相关公开资料:损失函数的“噪音免疫力” - 蝈蝈,文章:损失函数的“噪音免疫力”*。* 噪声分为两种: 对称噪...
内容提示: Robust Loss Functions under Label Noise forDeep Neural NetworksAritra GhoshMicrosoft, Bangalorearghosh@microsoft.comHimanshu KumarIndian Institute of Science, Bangalorehimanshukr@ee.iisc.ernet.inP. S. SastryIndian Institute of Science, Bangaloresastry@ee.iisc.ernet.inAbstractIn many ...
We study some of the widely used loss functions in deep networks and show that the loss function based on mean absolute value of error is inherently robust to label noise. Thus standard back propagation is enough to learn the true classifier even under label noise. Through experiments, we ...
Transductive inference on graphs such as label propagation algorithms is receiving a lot of attention. In this paper, we address a label propagation proble... T Kato,H Kashima,M Sugiyama 被引量: 0发表: 2008年 Robust Loss Functions under Label Noise for Deep Neural Networks We study some of...
bitempered_loss blur bnn_hmc bonus_based_exploration building_detection bustle c_learning cache_replacement caltrain capsule_em caql cascaded_networks cate cell_embedder cell_mixer cfq cfq_pt_vs_sa charformer ciw_label_noise class_balanced_distillation clay cluster_gcn clustering_normalized_cuts cnn_...
For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, or the performance is stable after adding some noise to the dataset. This repo contains a curated list of papers/articles and recent advancements in Robust Machine ...
where class corresponds to the readout neuron index of the correct label for a given sample. The loss is computed as the average of Nbatch training samples. This was repeated for a total of Nepochs. In order to improve generalisation, we added noise to the input by adding spikes following...
Code for the paper "A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise" (AAAI 2023) - GitHub - jongwooko/ALASCA: Code for the paper "A Gift from Label Smoothing: Robust Training with Ada
The proposed mixture correntropy is also a local similarity measure that not only improves the limitations of correntropy under a single kernel, but also handles heterogeneous data more flexibly and stably. The induced loss amalgamates the superiors of the state-of-the-art robust loss functions and...