Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey

11 December 2019

Papers citing "Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey"

29 / 29 papers shown

Title
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness Boqian Wu Q. Xiao Shunxin Wang N. Strisciuglio Mykola Pechenizkiy M. V. Keulen D. Mocanu Elena Mocanu OOD 3DH 52 0 0 03 Oct 2024
Data Quality in Edge Machine Learning: A State-of-the-Art Survey M. D. Belgoumri Mohamed Reda Bouadjenek Sunil Aryal Hakim Hacid 16 1 0 01 Jun 2024
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis Junlin Hou Jilan Xu Rui Feng Hao Chen 23 0 0 08 Apr 2024
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction Jianwei Wang Tianyin Wang Ziqian Zeng 46 1 0 28 Feb 2024
How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model Yixin Zhang Shen Zhao Han Gu Maciej Mazurowski VLM 30 4 0 17 Dec 2023
Sampling to Distill: Knowledge Transfer from Open-World Data Yuzheng Wang Zhaoyu Chen Jie M. Zhang Dingkang Yang Zuhao Ge Yang Liu Siao Liu Yunquan Sun Wenqiang Zhang Lizhe Qi 26 9 0 31 Jul 2023
Model Calibration in Dense Classification with Adaptive Label Perturbation Jiawei Liu Changkun Ye Shanpeng Wang Rui-Qing Cui Jing Zhang Kai Zhang Nick Barnes 29 5 0 25 Jul 2023
Multi-annotator Deep Learning: A Probabilistic Framework for Classification M. Herde Denis Huseljic Bernhard Sick 17 9 0 05 Apr 2023
How Accurate Does It Feel? -- Human Perception of Different Types of Classification Mistakes A. Papenmeier Dagmar Kern Daniel Hienert Yvonne Kammerer C. Seifert 11 18 0 13 Feb 2023
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels E. Santana G. Carneiro F. Cordeiro NoLa 18 6 0 23 Aug 2022
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation Lars Schmarje Vasco Grossmann Claudius Zelenka S. Dippel R. Kiko ... M. Pastell J. Stracke A. Valros N. Volkmann Reinahrd Koch 31 34 0 13 Jul 2022
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt Sören Mindermann J. Brauner Muhammed Razzak Mrinank Sharma Andreas Kirsch ... Benedikt Höltgen Aidan N. Gomez Adrien Morisot Sebastian Farquhar Y. Gal 25 148 0 14 Jun 2022
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance? Christoph Koller Goran Kauermann Xiao Xiang Zhu UQCV 11 6 0 30 May 2022
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification D. Zhu Michael A. Hedderich Fangzhou Zhai David Ifeoluwa Adelani Dietrich Klakow NoLa 25 32 0 20 Apr 2022
Maximum Likelihood Uncertainty Estimation: Robustness to Outliers Deebul Nair Nico Hochgeschwender Miguel A. Olivares-Mendez OOD 13 7 0 03 Feb 2022
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer Yongquan Yang Fengling Li Yani Wei Jie Chen Ning Chen Mohammad H. Alobaidi Hong Bu 6 8 0 20 Oct 2021
Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy Lars Schmarje Johannes Brunger M. Santarossa Simon-Martin Schroder R. Kiko Reinhard Koch 36 17 0 13 Oct 2021
Assessing the Quality of the Datasets by Identifying Mislabeled Samples Vaibhav Pulastya Gaurav Nuti Yash Kumar Atri Tanmoy Chakraborty NoLa 19 5 0 10 Sep 2021
EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling Jue Wang Haofan Wang Jincan Deng Weijia Wu Debing Zhang VLM CLIP 57 18 0 10 Sep 2021
A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering Lars Schmarje M. Santarossa Simon-Martin Schroder Claudius Zelenka R. Kiko J. Stracke N. Volkmann Reinhard Koch 22 10 0 30 Jun 2021
Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction Zehui Liao Yutong Xie Shishuai Hu Yong-quan Xia AI4CE 24 30 0 23 Apr 2021
Analysing the Noise Model Error for Realistic Noisy Label Data Michael A. Hedderich D. Zhu Dietrich Klakow NoLa 13 19 0 24 Jan 2021
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning Dimitrios Tanoglidis A. Ćiprijanović A. Drlica-Wagner 8 16 0 24 Nov 2020
A Survey of Label-noise Representation Learning: Past, Present and Future Bo Han Quanming Yao Tongliang Liu Gang Niu Ivor W. Tsang James T. Kwok Masashi Sugiyama NoLa 14 158 0 09 Nov 2020
Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph Jingkang Yang Weirong Chen Litong Feng Xiaopeng Yan Huabin Zheng Wayne Zhang NoLa 12 13 0 12 Oct 2020
Meta Soft Label Generation for Noisy Labels G. Algan ilkay Ulusoy NoLa 17 38 0 11 Jul 2020
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models Karishma Sharma Pinar E. Donmez Enming Luo Yan Liu I. Z. Yalniz NoLa 60 32 0 15 Mar 2020
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 47 172 0 24 May 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,659 0 09 Mar 2017