原文链接:Learning from Noisy Labels with Distillation 翻译工具:deepl 摘要 从噪声标签中学习的能力在许多视觉识别任务中是非常有用的,因为有噪声标签的大量数据是比较容易获得的。传统上,标签噪声被视为统计学上的离群值,并且已经提出了诸如importance re-weighting和bootstrapping等技术来缓解这一问题。根据我们的观...
Learning from noisy labels with deep neural networks. CoRR, abs/1406.2080, 2014.Sukhbaatar, S. and Fergus, R. Learning from noisy labels with deep neural networks. arXiv:1406.2080, 2014.Sukhbaatar, S., Fergus, R.: Learning from noisy labels with deep neural networks. CoRR abs/1406.2080 (...
第一种 Clean Data 是比较容易获取的，可以随便找现有的公开数据集，通过模拟置噪的方式来使数据集变成...
regularizer使得估计的标签转移概率收敛到真实的标注者混淆矩阵;pre-training: fine-tuning比train from scratch泛化性好很多;loss-based gradient clipping(梯度裁剪的噪声鲁棒版);early-learning(参数分类分别拟合干净和噪声标签,只惩罚其中一类参数);random noiseto open-set examples。
Learning from Noisy Labels with Deep Neural Networks We propose several simple approaches to training deep neural networks on data with noisy labels. We introduce an extra noise layer into the network which a... S Sukhbaatar,R Fergus - 《Eprint Arxiv》 被引量: 66发表: 2014年 TRAINING DEEP...
Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to ...
这是Google brain Samy Bengio 组里的工作,提出的观点并用实验证明:Deep neural networks easily fit random labels。这个观点几乎是 2017 年之后 noisy label 相关文章必引观点。 这篇文章之前,introduction 都在介绍众包 & 错误不可避免...
Learning from Noisy Labels with Deep Neural Networks: A Survey. arXiv 2020 paper bib Hwanjun Song, Minseok Kim, Dongmin Park, Jae-Gil Lee Multimodal Intelligence: Representation Learning, Information Fusion, and Applications. IEEE Journal of Selected Topics in Signal Processing 2020 paper bib Cha...
Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose ...
Learning from Noisy Labels with Deep Neural Networks: A Survey. arXiv 2020 paper bib Hwanjun Song, Minseok Kim, Dongmin Park, Jae-Gil Lee Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey. arXiv 2020 paper bib Samuel Henrique Silva, Peyman Najafirad Privacy in De...