Classifier‐Guided Visual Correction of Noisy Labels for Image Classification Tasks

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: Computer Graphics Forum ; 39 (2020), 3. - S. 195-205

Keyword: Maschinelles Lernen
Expertensystem
Mensch-Maschine-Kommunikation

Event: Veröffentlichung

(where): Ulm

(who): Universität Ulm

(when): 2020

Creator: Bäuerle, Alex
Neumann, Heiko
Ropinski, Timo

DOI: 10.18725/OPARU-43002

URN: urn:nbn:de:bsz:289-oparu-43078-3

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:24 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Bäuerle, Alex
Neumann, Heiko
Ropinski, Timo
Universität Ulm

Time of origin

2020

Other Objects (12)

A teacher-guided early-learning method for medical image segmentation from noisy labels

Robust optimal classification trees under noisy labels

SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels

Semi-supervised classifier guided by discriminator

Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Separating hard clean samples from noisy samples with samples’ learning risk for DNN when learning with noisy labels

MMCo: using multimodal deep learning to detect malicious traffic with noisy labels

Evaluating deep learning models for classifying OCT images with limited data and noisy labels

From labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Hochschulschrift

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

Hochschulschrift

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

A teacher-guided early-learning method for medical image segmentation from noisy labels

Robust optimal classification trees under noisy labels

SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels

Semi-supervised classifier guided by discriminator

Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Separating hard clean samples from noisy samples with samples’ learning risk for DNN when learning with noisy labels

MMCo: using multimodal deep learning to detect malicious traffic with noisy labels

Evaluating deep learning models for classifying OCT images with limited data and noisy labels

From labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Hochschulschrift

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

Hochschulschrift

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

A teacher-guided early-learning method for medical image segmentation from noisy labels

Robust optimal classification trees under noisy labels

SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels

Semi-supervised classifier guided by discriminator

Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Separating hard clean samples from noisy samples with samples’ learning risk for DNN when learning with noisy labels

MMCo: using multimodal deep learning to detect malicious traffic with noisy labels

Evaluating deep learning models for classifying OCT images with limited data and noisy labels

From labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Hochschulschrift

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

Hochschulschrift

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Classifier‐Guided Visual Correction of Noisy Labels for Image Classification Tasks

Object Details

References and Relationships

Classification and Topics

Contributors, Places and Time

Further information

Data provider

Associated

Time of origin

Other Objects (12)

A teacher-guided early-learning method for medical image segmentation from noisy labels

Robust optimal classification trees under noisy labels

SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels

Semi-supervised classifier guided by discriminator

Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Separating hard clean samples from noisy samples with samples’ learning risk for DNN when learning with noisy labels

MMCo: using multimodal deep learning to detect malicious traffic with noisy labels

Evaluating deep learning models for classifying OCT images with limited data and noisy labels

From labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

A teacher-guided early-learning method for medical image segmentation from noisy labels

Robust optimal classification trees under noisy labels

SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels

Semi-supervised classifier guided by discriminator

Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Separating hard clean samples from noisy samples with samples’ learning risk for DNN when learning with noisy labels

MMCo: using multimodal deep learning to detect malicious traffic with noisy labels

Evaluating deep learning models for classifying OCT images with limited data and noisy labels

From labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

A teacher-guided early-learning method for medical image segmentation from noisy labels

Robust optimal classification trees under noisy labels

SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels

Semi-supervised classifier guided by discriminator

Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Separating hard clean samples from noisy samples with samples’ learning risk for DNN when learning with noisy labels

MMCo: using multimodal deep learning to detect malicious traffic with noisy labels

Evaluating deep learning models for classifying OCT images with limited data and noisy labels

From labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

Robustness analysis of deep neural networks in the presence of adversarial perturbations and noisy labels

Related objects

Reset password