NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning | Paper Citation Record

Observation c77c11ee-4a88-44c2-b491-353b3ca41a04 · outbound

This paper cites Food-101 – mining discriminative components with random forests.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Food-101 – mining discriminative components with random forests

Reference 1

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Observation f7fb3e0f-bafa-488f-832b-e1d1232317cf · outbound

This paper cites Bilateral Sharpness-Aware Minimization for Flatter Minima.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Bilateral Sharpness-Aware Minimization for Flatter Minima

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Observation 78a79d3d-5a4d-430a-ae84-17ca7d77b157 · outbound

This paper cites Generalized jensen-shannon divergence loss for learning with noisy labels.Advances in Neural Information Processing Systems, 34:30284–30297,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Generalized jensen-shannon divergence loss for learning with noisy labels.Advances in Neural Information Processing Systems, 34:30284–30297,

Reference 9

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Observation d5118454-5b9f-4ed5-a669-6f02cb63c99e · outbound

This paper cites Sharpness-aware min- imization for efficiently improving generalization.Inter- national Conference on Learning Representations,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Sharpness-aware min- imization for efficiently improving generalization.Inter- national Conference on Learning Representations,

Reference 10

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Observation 80e030f4-b7c4-4fd8-a480-8125c607acef · outbound

This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels.Advances in neural information processing systems, 31,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Co-teaching: Robust training of deep neural networks with extremely noisy labels.Advances in neural information processing systems, 31,

Reference 11

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Observation 88d23a42-c804-4d0c-b974-6b9e8c5bd903 · outbound

This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning On large-batch training for deep learning: Generalization gap and sharp minima

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Observation 5189a60a-3b0e-4b00-afef-f39821c7b8da · outbound

This paper cites Learning multiple layers of features from tiny im- ages.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Learning multiple layers of features from tiny im- ages

Reference 17

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Observation ed8ec372-6860-41b1-8903-d55a29eae80e · outbound

This paper cites Tiny ima- genet visual recognition challenge.CS 231N, 7(7):3,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Tiny ima- genet visual recognition challenge.CS 231N, 7(7):3,

Reference 19

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Observation a2d848a0-2042-4698-bb23-3db4f315e544 · outbound

This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 21

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Observation 278ee0d3-e365-47d4-bf21-4a8a38845d25 · outbound

This paper cites Learning from noisy data with robust representation learning.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Learning from noisy data with robust representation learning

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Observation 74ea2fc3-fd89-4af4-a469-3871843dd46a · outbound

This paper cites Disc: Learning from noisy labels via dynamic instance-specific selection and correction.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Disc: Learning from noisy labels via dynamic instance-specific selection and correction

Reference 23

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Observation 6ab16cea-da61-45ba-8891-4c1445f147aa · outbound

This paper cites Peer loss functions: Learning from noisy labels without knowing noise rates.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Peer loss functions: Learning from noisy labels without knowing noise rates

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Observation 5a4c883c-0178-4446-aaf8-05cbc1e2635d · outbound

This paper cites Balanced sharpness-aware minimization for im- balanced regression.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Balanced sharpness-aware minimization for im- balanced regression

Reference 25

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Observation eba8f418-3fd2-4371-8773-f047e26fe4aa · outbound

This paper cites Loss factorization, weakly supervised learning and label noise robustness.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Loss factorization, weakly supervised learning and label noise robustness

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Observation 0e0e83db-7f15-438b-9a06-99113ccee51f · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Making deep neural networks robust to label noise: A loss correction approach

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Observation e15fd1d7-5436-49dd-8100-8dac2d204c78 · outbound

This paper cites Learning to reweight examples for robust deep learning.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Learning to reweight examples for robust deep learning

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Observation 581c1608-e7d8-4a0a-8a81-cfb68a15a531 · outbound

This paper cites Noisy concurrent training for effi- cient learning under label noise.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Noisy concurrent training for effi- cient learning under label noise

Reference 30

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Observation 4caa3a69-77c3-4a64-9e67-99a9450b624f · outbound

This paper cites Classification with asymmetric label noise: Consistency and maximal denoising.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Classification with asymmetric label noise: Consistency and maximal denoising

Reference 31

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Observation 952fc2e0-6a8a-46d5-8e2f-ac4608335560 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Very Deep Convolutional Networks for Large-Scale Image Recognition

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Observation 91bf79c3-1729-46ec-bb89-c211d536d64e · outbound

This paper cites SELFIE: Refurbishing unclean samples for robust deep learning.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning SELFIE: Refurbishing unclean samples for robust deep learning

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Observation f298edad-9c3f-4c70-8536-ceff1d78cfbf · outbound

This paper cites Joint optimization framework for learning with noisy labels.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Joint optimization framework for learning with noisy labels

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Observation 4c25d0fb-51ab-4ddf-9ffa-4c0bfc523cc3 · outbound

This paper cites Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results.Advances in neural information pro- cessing systems, 30,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results.Advances in neural information pro- cessing systems, 30,

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Observation 907a9860-14db-4602-b955-77ea73cd7477 · outbound

This paper cites Snuba: Automating weak supervision to label training data.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Snuba: Automating weak supervision to label training data

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Observation 08c68121-a0d0-4be2-ae5c-c2c60e013821 · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Symmetric cross entropy for robust learning with noisy labels

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Observation e1b57c64-5f3b-4220-a0e2-5285dde151a5 · outbound

This paper cites Combating noisy labels by agreement: A joint training method with co-regularization.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Combating noisy labels by agreement: A joint training method with co-regularization

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Observation 100d0c94-c102-46a2-913c-7301a6df23a6 · outbound

This paper cites Are anchor points really indispensable in label-noise learn- ing?Advances in neural information processing systems, 32,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Are anchor points really indispensable in label-noise learn- ing?Advances in neural information processing systems, 32,

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Observation 24fac4ed-93b4-4a1e-a479-8b9feaca3f82 · outbound

This paper cites Learning from massive noisy labeled data for image classification.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Learning from massive noisy labeled data for image classification

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Observation 3bce0185-9229-418f-8b9f-6b539b2d1438 · outbound

This paper cites Label correction using contrastive proto- typical classifier for noisy label learning.Information Sci- ences, 649:119647,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Label correction using contrastive proto- typical classifier for noisy label learning.Information Sci- ences, 649:119647,

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Observation 29c515a6-c3f8-4e45-ada7-f04d49bbd723 · outbound

This paper cites How does dis- agreement help generalization against label corruption? InInternational conference on machine learning, pages 7164–7173.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning How does dis- agreement help generalization against label corruption? InInternational conference on machine learning, pages 7164–7173

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Observation 37636dc6-a66b-417d-ab45-7ab6c972062d · outbound

This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels.Advances in neural informa- tion processing systems, 31,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Generalized cross entropy loss for training deep neural networks with noisy labels.Advances in neural informa- tion processing systems, 31,

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Observation 4f3d610f-e69c-4182-bdd5-8d6bc81575c5 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning mixup: Beyond Empirical Risk Minimization

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Observation c0155e87-e9e0-4004-bb27-bb236eb47b0e · outbound

This paper cites mixup: Beyond empirical risk minimization.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning mixup: Beyond empirical risk minimization

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Observation 63fcd7bd-33ea-4b86-9135-2351643ceb81 · outbound

This paper cites Learn- ing with feature-dependent label noise: A progressive ap- proach.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Learn- ing with feature-dependent label noise: A progressive ap- proach

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Observation 7fd03e8e-70f7-4c11-a4e9-99acff24caf2 · outbound

This paper cites Centrality and consistency: Two-stage clean samples identification for learning with instance-dependent noisy labels.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Centrality and consistency: Two-stage clean samples identification for learning with instance-dependent noisy labels

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Observation 26420005-c35b-4dae-9c42-27f284565b0b · outbound

This paper cites Error-bounded correction of noisy labels.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Error-bounded correction of noisy labels

Reference 50

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Observation 0f6ea24e-3ae8-4416-9f96-1f519d28c5ce · outbound

This paper cites Meta label correction for noisy label learning.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Meta label correction for noisy label learning

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Observation 78150c14-5130-4b6f-b1a8-6e4032d8e9c1 · outbound

This paper cites Curriculum learning by dynamic instance hard- ness.Advances in Neural Information Processing Systems, 33:8602–8613,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Curriculum learning by dynamic instance hard- ness.Advances in Neural Information Processing Systems, 33:8602–8613,

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This paper cites Asymmetric loss functions for noise-tolerant learning: Theory and applica- tions.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(7):8094–8109,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Asymmetric loss functions for noise-tolerant learning: Theory and applica- tions.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(7):8094–8109,

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Observation cac89339-8d13-43a2-9dbf-4ee7b92e616d · outbound

This paper cites A second-order approach to learning with instance- dependent label noise.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning A second-order approach to learning with instance- dependent label noise

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Observation feb09336-90de-4a69-9ae8-dbbf4b7171e3 · outbound

This paper cites Meta- weight-net: Learning an explicit mapping for sample weighting.Advances in neural information processing sys- tems, 32,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Meta- weight-net: Learning an explicit mapping for sample weighting.Advances in neural information processing sys- tems, 32,

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Observation 605b1669-926a-43c8-98c0-612b8ee43803 · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Temporal Ensembling for Semi-Supervised Learning

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Observation 697d07a4-8b14-4c61-9da6-97bcea9c6112 · outbound

This paper cites Pac-bayesian generalisation error bounds for gaussian process classification.Journal of machine learning research, 3(Oct):233–269,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Pac-bayesian generalisation error bounds for gaussian process classification.Journal of machine learning research, 3(Oct):233–269,

Reference 2013

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Observation a42e48b4-ddaf-4728-af9c-171fe7a27d7e · outbound

This paper cites Understanding and utilizing deep neural networks trained with noisy labels.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Understanding and utilizing deep neural networks trained with noisy labels

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Observation db3f2324-e975-4394-acb8-e5fd92817542 · outbound

This paper cites Visualizing the loss landscape of neural nets.Advances in neural information processing systems, 31,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Visualizing the loss landscape of neural nets.Advances in neural information processing systems, 31,

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Observation 509fb31c-5921-4a24-95a4-2f25f6615c84 · outbound

This paper cites Asymmetric valleys: Beyond sharp and flat local minima.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Asymmetric valleys: Beyond sharp and flat local minima

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Observation dcc7f456-953d-492f-b0d1-4e3891c67728 · outbound

This paper cites Sharp minima can general- ize for deep nets.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Sharp minima can general- ize for deep nets

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Observation bed4cbab-3218-425b-ad8b-0f846777fabd · outbound

This paper cites Deep residual learning for image recog- nition.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Deep residual learning for image recog- nition

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Observation 7356572c-b3e3-4049-8ac9-1f65ac2f437b · outbound

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NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Beyond class- conditional assumption: A primary attempt to combat instance-dependent label noise

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Observation 4069eb29-5a78-4105-b767-2f5c2300dd75 · outbound

This paper cites Learning with instance-dependent label noise: A sample sieve approach.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Learning with instance-dependent label noise: A sample sieve approach

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Observation 10e4f5ca-23de-4860-8a0f-35b3a627e8d0 · outbound

This paper cites Learning with Instance-Dependent Label Noise: A Sample Sieve Approach.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Learning with Instance-Dependent Label Noise: A Sample Sieve Approach

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Observation 02632347-db16-4c5b-86f5-adbd1e636f6c · outbound

This paper cites Combining layered label correction and mixup super- vised contrastive learning to learn noisy labels.Informa- tion Sciences, 642:119242,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Combining layered label correction and mixup super- vised contrastive learning to learn noisy labels.Informa- tion Sciences, 642:119242,

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Observation b81ad490-87a8-4948-8702-c9ae782aa724 · outbound

This paper cites Unicon: Combating label noise through uniform selection and contrastive learning.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Unicon: Combating label noise through uniform selection and contrastive learning

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Observation d54e9fb8-6276-42fc-85e6-7cd9a21df049 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Improved Regularization of Convolutional Neural Networks with Cutout

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Observation 4e67933e-5c88-4f45-bfa0-25766a5b0e20 · outbound

This paper cites Exploring generalization in deep learning.Advances in neural infor- mation processing systems, 30,.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning Exploring generalization in deep learning.Advances in neural infor- mation processing systems, 30,

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