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1911.08059
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How does Early Stopping Help Generalization against Label Noise?
19 November 2019
Hwanjun Song
Minseok Kim
Dongmin Park
Jae-Gil Lee
NoLa
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Papers citing
"How does Early Stopping Help Generalization against Label Noise?"
43 / 43 papers shown
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Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision
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277
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Instance-Dependent Noisy Label Learning via Graphical Modelling
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Cuong C. Nguyen
Rafael Felix
Thanh-Toan Do
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206
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Bongsoo Yi
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232
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Robust Training under Label Noise by Over-parameterization
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Zhihui Zhu
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256
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Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
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Chuang Zhu
Yi Chen
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Tiejun Huang
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372
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Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
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Kwanghee Choi
Joonyoung Yi
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356
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Confidence Adaptive Regularization for Deep Learning with Noisy Labels
Yangdi Lu
Yang Bo
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208
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Disparity Between Batches as a Signal for Early Stopping
Mahsa Forouzesh
Patrick Thiran
278
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How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?
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Christopher Kanan
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158
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Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
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Dong Lee
Sae-Young Chung
178
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22 Jun 2021
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels
Seulki Park
Hwanjun Song
Daeho Um
D. Jo
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J. Choi
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341
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14 Jun 2021
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Florian Dubost
Erin Hong
Max Pike
Siddharth Sharma
Siyi Tang
Nandita Bhaskhar
Christopher Lee-Messer
D. Rubin
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254
2
0
03 Jun 2021
Generation and Analysis of Feature-Dependent Pseudo Noise for Training Deep Neural Networks
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Sree Ram Kamabattula
Kumudha Musini
Babak Namazi
G. Sankaranarayanan
V. Devarajan
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88
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22 May 2021
FINE Samples for Learning with Noisy Labels
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Taehyeon Kim
Jongwoo Ko
Sangwook Cho
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301
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Noah Golowich
Ravi Kumar
Pasin Manurangsi
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335
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Understanding Instance-Level Label Noise: Disparate Impacts and Treatments
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139
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Learning to Combat Noisy Labels via Classification Margins
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Jelena Bradic
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199
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203
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Semi-supervised novelty detection using ensembles with regularized disagreement
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Robust Learning by Self-Transition for Handling Noisy Labels
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276
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Learning from Noisy Labels with Deep Neural Networks: A Survey
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19 Nov 2019
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