(2022). Robust Training under Label Noise by Over-parameterization. Proceedings of the 39th International Conference on Machine Learning. 关键词: 压缩感知, ODE, 过参数网络, 隐式正则 0. 摘要翻译 近期,随着神经网络的网络参数量越来越多于训练样本量,过参数化的深度网络主主导了现代机器学习任务的性能。
一般而言,training samples带噪声的方式有两种,一是在 data points上加 Gaussian noise,二是 label noise,在分类任务中,它指的是有一定概率label是错的,而在回归任务中,它指的是在 clean label 上额外加一个sparse 的noise。如何在training samples带有噪声的情况下提高过参数神经网络的鲁棒性就是一个非常重要的...
Benefiting from the unsupervised dual-consistency learning strategy, we can obtain robust representations to combat label noise. Further, we impose a robust consistency regularization technique on the predictions of the classifiers to improve the whole network's robustness. Comprehensive evaluations on ...
In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network...
Federated Learning Under Statistical Heterogeneity on Riemannian Manifolds Chapter © 2023 FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients Chapter © 2025 Federated Learning for Assigning Weights to Clients on Long-Tailed Data Chapter © ...
S. SastryIndian Institute of Science, Bangaloresastry@ee.iisc.ernet.inAbstractIn many applications of classif i er learning, training data suf f ers fromlabel noise. Deep networks are learned using huge training data where theproblem of noisy labels is particularly relevant. The current ...
We propose a principled approach for robust training of over-parameterized deep networks in classification tasks where a proportion of training labels are corrupted. The main idea is yet very simple: label noise is sparse and incoherent with the network learned from clean data, so we model the ...
The results of long-tailed classification. 4.3 Learning with noisy labels We provide convenient and comprehensive commands in ./run/animal10N and ./run/Food101N to train and test our method with noisy labels. Animal-10N python3 main.py \ --batch-size 128 \ --gpu 0 \ --epochs 200 \ ...
20220722 007 链接: https://arxiv.org/abs/2202.14026作者:Sheng Liu, Zhihui Zhu, Qing Qu, Chong You Affiliations:Center for Data Science, New York University; Electrical and Computer Engineering, U…
This repository contains code for AAAI-23 Paper "A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise"How to useAll steps start from the root directory.Set environment setup pip...