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Iterative Learning with Open-set Noisy Labels
[article]
2018
arXiv
pre-print
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To address this problem, we propose a novel iterative learning framework for training CNNs on datasets with open-set noisy labels. ...
Our approach detects noisy labels and learns deep discriminative features in an iterative fashion. ...
In this paper, we propose an iterative learning framework that can robustly train CNNs on datasets with open-set noisy labels. ...
arXiv:1804.00092v1
fatcat:56cvrf2yujftllyqs6dcocjste
Iterative Learning with Open-set Noisy Labels
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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To address this problem, we propose a novel iterative learning framework for training CNNs on datasets with open-set noisy labels. ...
Our approach detects noisy labels and learns deep discriminative features in an iterative fashion. ...
In this paper, we propose an iterative learning framework that can robustly train CNNs on datasets with open-set noisy labels. ...
doi:10.1109/cvpr.2018.00906
dblp:conf/cvpr/WangLMBZSX18
fatcat:ys5xxa6jpvh2fi7evpwqvc77v4
Deep Learning Classification With Noisy Labels
[article]
2020
arXiv
pre-print
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Deep Learning systems have shown tremendous accuracy in image classification, at the cost of big image datasets. Collecting such amounts of data can lead to labelling errors in the training set. ...
We review recent works on how to manage noisy annotations when training deep learning classifiers, independently from our interest in face recognition. ...
open-set and closed-set noise. ...
arXiv:2004.11116v1
fatcat:zp4cuhuxpbgxxihgecvqtrlx2q
Deep Learning Classification with Noisy Labels
2020
2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
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open-set and closed-set noise. ...
Finally, Iterative Noise Filtering [15] , assumes that the class with the highest prediction for the unlabeled examples is correct. Deep Self-Learning [20] learns an initial net on noisy labels. ...
doi:10.1109/icmew46912.2020.9105992
dblp:conf/icmcs/SanchezGMB20
fatcat:f4bhvsqu35db3mn4btbbcjabiu
Attention-Aware Noisy Label Learning for Image Classification
[article]
2020
arXiv
pre-print
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In this paper, the attention-aware noisy label learning approach (A^2NL) is proposed to improve the discriminative capability of the network trained on datasets with potential label noise. ...
The cheapest way to obtain a large body of labeled visual data is to crawl from websites with user-supplied labels, such as Flickr. ...
The goal of the proposed method does not end with predicting the noisy labelsỹ or learning the noisy distributions, instead it is to predict the true labels given the image sets with noisy labels. ...
arXiv:2009.14757v1
fatcat:7f7ympduavhlhn2xqluyfwquxy
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
[article]
2020
arXiv
pre-print
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In this work, we study a new variant of the noisy label problem that combines the open-set and closed-set noisy labels, and introduce a benchmark evaluation to assess the performance of training algorithms ...
However, acquiring such large-scale data sets with precise annotations is very expensive and time-consuming, and the cheap alternatives often yield data sets that have noisy labels. ...
The learning with open-set noisy labels has only recently been explored by Wang et al. ...
arXiv:2011.05704v1
fatcat:7y2yn3fpcfarvfoondzzpq4y2y
Noise-resistant Deep Metric Learning with Ranking-based Instance Selection
[article]
2021
arXiv
pre-print
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Although much research effort has been devoted to improving robustness to noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains open. ...
The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. ...
In classification, [47] simulates open-set noisy labels by adding data from other datasets. In this paper, we propose the Small Cluster noise model for open-set label noise in metric learning. ...
arXiv:2103.16047v2
fatcat:es66xlztzbc4rovic432bzxtla
S3: Supervised Self-supervised Learning under Label Noise
[article]
2021
arXiv
pre-print
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In this context, in this paper we address the problem of classification in the presence of label noise and more specifically, both close-set and open-set label noise, that is when the true label of a sample ...
Despite the large progress in supervised learning with Neural Networks, there are significant challenges in obtaining high-quality, large-scale and accurately labeled datasets. ...
Clearly, for an open-set noisy label it is the case that y i = y i , y i / ∈ {0, 1} K , while for closed-set noisy samples y i = y i , y i ∈ {0, 1} K . ...
arXiv:2111.11288v1
fatcat:lqudfwtx5zcffelsuxvhew4wwq
Automatic Error Correction for Speaker Embedding Learning with Noisy Labels
2021
Conference of the International Speech Communication Association
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However, noisy labels often occur during data collection. In this paper, we propose an automatic error correction method for deep speaker embedding learning with noisy labels. ...
Moreover, when combining with the Bayesian estimation of PLDA with noisy training labels at the back-end, the whole system performs better under conditions in which noisy labels are present. ...
Related works
Network learning with noisy labels Types of label noise can be categorized into two categories: closed-set noise and open-set noise. ...
doi:10.21437/interspeech.2021-2021
dblp:conf/interspeech/TongLLWLH21
fatcat:6ynqdzftlrckhbzuvo3fbu42ey
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling
[article]
2022
arXiv
pre-print
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)Iterative refinement labeling further iteratively refines the labels of noisy samples and then combines the learned predictors to be robust to the unseen and noisy samples. ...
set of machine learning based competitors on the Qlib platform. ...
Thus, it inspires us to further denoise the labels of noisy samples with multiple iterative evaluations. ...
arXiv:2107.11972v2
fatcat:or3ymqiljjhyrk5zc74ll37bme
Label Refinement with an Iterative Generative Adversarial Network for Boosting Retinal Vessel Segmentation
[article]
2019
arXiv
pre-print
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Our iterative GAN is trained based on a set of high-quality patches (i.e. with consistent manual labels among different observers) and low-quality patches with noisy manual vessel labels. ...
To this end, we have developed a novel label refinement method based on an iterative generative adversarial network (GAN). ...
Our training data is collected based on a set of high-quality retinal patches with consistent manual labels among different observers and low-quality retinal patches with noisy manual vessel labels. ...
arXiv:1912.02589v1
fatcat:ffewyn434bhxlfdjeyvmlu2xhm
Sample Noise Impact on Active Learning
[article]
2021
arXiv
pre-print
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This work explores the effect of noisy sample selection in active learning strategies. ...
We hope that the questions raised in this paper are of interest to the community and could open new paths for active learning research. ...
We use KCenterGreedy (KCenter) as a proxy for Core-sets [6] since there is no open implementation available. ...
arXiv:2109.01372v1
fatcat:f4jav3ap6bcqznyjeotn3dhlnm
Learning from Noisy Labels with No Change to the Training Process
2021
International Conference on Machine Learning
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There has been much interest in recent years in developing learning algorithms that can learn accurate classifiers from data with noisy labels. ...
A widely-studied noise model is that of classconditional noise (CCN), wherein a label y is flipped to a label y with some associated noise probability that depends on both y and y. ...
The primary challenge in learning from noisy labels is to design algorithms which, despite being given data with noisy labels as input, can learn accurate classifiers for the true, clean distribution. ...
dblp:conf/icml/ZhangL021
fatcat:nma4wppejvfcfphxbhmjj57kra
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
[article]
2021
arXiv
pre-print
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Learning with noisy labels is a practically challenging problem in weakly supervised learning. ...
In this paper, we empirically show that open-set noisy labels can be non-toxic and even benefit the robustness against inherent noisy labels. ...
It is worthy to note that open-set noisy labels do not encounter this issue. Learning with inherent noisy labels. ...
arXiv:2106.10891v3
fatcat:2vjoezev7za4bbmzz5ipqakp5y
How does Disagreement Help Generalization against Label Corruption?
[article]
2019
arXiv
pre-print
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Learning with noisy labels is one of the hottest problems in weakly-supervised learning. ...
Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of Titan Xp GPU used for this research. ...
arXiv:1901.04215v3
fatcat:fugxwrwouja3focmitlgsxze2y
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