Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition | Paper Citation Record

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This paper cites Explaining and Harnessing Adversarial Examples.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Explaining and Harnessing Adversarial Examples

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This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Are we ready for autonomous driving? the kitti vision benchmark suite

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This paper cites Waldstein, Ursula Schmidt-Erfurth, and Georg Langs.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Waldstein, Ursula Schmidt-Erfurth, and Georg Langs

Reference 3

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This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

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This paper cites Generalized out-of-distribution detection: A survey.International Journal of Computer Vision, 132(12):5635–5662, 2024.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Generalized out-of-distribution detection: A survey.International Journal of Computer Vision, 132(12):5635–5662, 2024

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Unresolved cited work

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This paper cites Scaling out-of-distribution detection for real- world settings.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Scaling out-of-distribution detection for real- world settings

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This paper cites Dice: Leveraging sparsification for out-of-distribution detection.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Dice: Leveraging sparsification for out-of-distribution detection

Reference 8

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This paper cites React: Out-of-distribution detection with rectified activations.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition React: Out-of-distribution detection with rectified activations

Reference 9

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This paper cites Neural mean discrep- ancy for efficient out-of-distribution detection.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Neural mean discrep- ancy for efficient out-of-distribution detection

Reference 10

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This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 11

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This paper cites Out-of-distribution detection with deep nearest neighbors.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Out-of-distribution detection with deep nearest neighbors

Reference 12

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This paper cites How to exploit hyperspherical embed- dings for out-of-distribution detection?, 2023.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition How to exploit hyperspherical embed- dings for out-of-distribution detection?, 2023

Reference 13

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This paper cites SSD: A Unified Framework for Self-Supervised Outlier Detection.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition SSD: A Unified Framework for Self-Supervised Outlier Detection

Reference 14

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This paper cites How to overcome curse-of-dimensionality for out-of-distribution detection?Proceedings of the AAAI Conference on Artificial Intelligence, 38(18):19849–19857, Mar.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition How to overcome curse-of-dimensionality for out-of-distribution detection?Proceedings of the AAAI Conference on Artificial Intelligence, 38(18):19849–19857, Mar

Reference 15

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This paper cites Amazon’s mechanical turk: A new source of inexpensive, yet high-quality data? 2016.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Amazon’s mechanical turk: A new source of inexpensive, yet high-quality data? 2016

Reference 16

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Unresolved cited work

Reference 17

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This paper cites Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153, 2022.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153, 2022

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This paper cites Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien

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This paper cites Understanding deep learning requires rethinking generalization.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Understanding deep learning requires rethinking generalization

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This paper cites Classification with noisy labels by importance reweighting.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Classification with noisy labels by importance reweighting

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

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Making deep neural networks robust to label noise: A loss correction approach

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This paper cites Provably end-to-end label-noise learning without anchor points.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Provably end-to-end label-noise learning without anchor points

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This paper cites Part-dependent label noise: Towards instance- dependent label noise.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Part-dependent label noise: Towards instance- dependent label noise

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This paper cites Estimating instance-dependent Bayes-label transition matrix using a deep neural network.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Estimating instance-dependent Bayes-label transition matrix using a deep neural network

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This paper cites Learning with bounded instance and label-dependent label noise.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Learning with bounded instance and label-dependent label noise

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This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels.Advances in neural information processing systems, 31, 2018.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Generalized cross entropy loss for training deep neural networks with noisy labels.Advances in neural information processing systems, 31, 2018

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Curriculum Loss: Robust Learning and Generalization against Label Corruption

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

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Symmetric cross entropy for robust learning with noisy labels

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This paper cites Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels

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

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition How does disagreement help generalization against label corruption? InInternational conference on machine learning, pages 7164–7173

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition SELF: Learning to Filter Noisy Labels with Self-Ensembling

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Co-teaching: Robust training of deep neural networks with extremely noisy labels

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition DivideMix: Learning with Noisy Labels as Semi-supervised Learning

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Learning from noisy labels by regularized estimation of annotator confusion

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Deep learning from crowdsourced labels: Coupled cross-entropy minimization, identifiability, and regularization

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This paper cites Robust principal component analysis?Journal of the ACM (JACM), 58(3):1–37, 2011.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Robust principal component analysis?Journal of the ACM (JACM), 58(3):1–37, 2011

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Image classification by non-negative sparse coding, low-rank and sparse decomposition

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Candes, Xiaodong Li, Yi Ma, and John Wright

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition A new alternating minimization algorithm for total variation image reconstruction.SIAM J

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This paper cites A randomized algorithm for principal component analysis.SIAM Journal on Matrix Analysis and Applications, 31(3):1100–1124, 2010.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition A randomized algorithm for principal component analysis.SIAM Journal on Matrix Analysis and Applications, 31(3):1100–1124, 2010

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This paper cites Subspace iteration randomization and singular value problems.SIAM Journal on Scientific Computing, 37(3):A1139–A1173, 2015.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Subspace iteration randomization and singular value problems.SIAM Journal on Scientific Computing, 37(3):A1139–A1173, 2015

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This paper cites Learning multiple layers of features from tiny images.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Learning multiple layers of features from tiny images

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This paper cites Learning with noisy labels revisited: A study using real-world human annotations.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Learning with noisy labels revisited: A study using real-world human annotations

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition SELFIE: Refurbishing unclean samples for robust deep learning

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Unresolved cited work

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This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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This paper cites Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop, 2016.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop, 2016

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:05.644494Z

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This paper cites End-to-end Convolutional Network for Saliency Prediction.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition End-to-end Convolutional Network for Saliency Prediction

Reference 49

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This paper cites Describing Textures in the Wild.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Describing Textures in the Wild

Reference 50

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This paper cites Places: An Image Database for Deep Scene Understanding.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Places: An Image Database for Deep Scene Understanding

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:05.297120Z

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This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition A baseline for detecting misclassified and out-of-distribution examples in neural networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:05.630523Z

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This paper cites Energy-based Out-of-distribution Detection.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Energy-based Out-of-distribution Detection

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:05.305340Z

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This paper cites Supervised contrastive learning.Advances in neural information processing systems, 33:18661–18673, 2020.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Supervised contrastive learning.Advances in neural information processing systems, 33:18661–18673, 2020

Reference 54

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This paper cites Provably end-to-end label-noise learning without anchor points.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Provably end-to-end label-noise learning without anchor points

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:05.606488Z

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This paper cites Symmetric cross entropy for robust learning with noisy labels, 2019.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Symmetric cross entropy for robust learning with noisy labels, 2019

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:05.317668Z

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:55:05.583121Z

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:55:05.569316Z

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Observation 9235bd33-a4ce-4ed1-8f1b-130ed087a36b · outbound

This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels, 2018.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Co-teaching: Robust training of deep neural networks with extremely noisy labels, 2018

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:05.555828Z

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This paper cites Densely Connected Convolutional Networks.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition Densely Connected Convolutional Networks

Reference 60

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