Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning

• URL: http://arxiv.org/abs/2212.00479v2
• Date: Wed, 17 Jul 2024 05:28:13 GMT
• Title: Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning
• Authors: Hansang Lee, Haeil Lee, Helen Hong, Junmo Kim,
• Abstract summary: We propose a method of learning noisy label data using the label noise selection with test-time augmentation (TTA) cross-entropy and classifier learning with the NoiseMix method. In experiments on the ISIC-18 public skin lesion diagnosis dataset, the proposed TTA cross-entropy outperformed the conventional cross-entropy and the TTA uncertainty in detecting label noise data.
• Score: 22.02829139522153
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As the size of the dataset used in deep learning tasks increases, the noisy label problem, which is a task of making deep learning robust to the incorrectly labeled data, has become an important task. In this paper, we propose a method of learning noisy label data using the label noise selection with test-time augmentation (TTA) cross-entropy and classifier learning with the NoiseMix method. In the label noise selection, we propose TTA cross-entropy by measuring the cross-entropy to predict the test-time augmented training data. In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the noisy and the clean label data. In experiments on the ISIC-18 public skin lesion diagnosis dataset, the proposed TTA cross-entropy outperformed the conventional cross-entropy and the TTA uncertainty in detecting label noise data in the label noise selection process. Moreover, the proposed NoiseMix not only outperformed the state-of-the-art methods in the classification performance but also showed the most robustness to the label noise in the classifier learning.

Related papers

• Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection [84.78475642696137]
  The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. We propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS) SGPS constructs reliable positive pairs for noisy samples to enhance the sample utilization.
  arXiv  Detail & Related papers  (2025-01-19T14:41:55Z)
• Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks [1.261491746208123]
  This study explores the impact of label noise on gradient-boosted decision trees (GBDTs) We adapt two noise detection methods from deep learning for use with GBDTs and introduce a new detection method called Gradients. Our noise detection methods achieve state-of-the-art results, with a noise detection accuracy above 99% on the Adult dataset across all noise levels.
  arXiv  Detail & Related papers  (2024-09-13T09:09:24Z)
• Extracting Clean and Balanced Subset for Noisy Long-tailed Classification [66.47809135771698]
  We develop a novel pseudo labeling method using class prototypes from the perspective of distribution matching. By setting a manually-specific probability measure, we can reduce the side-effects of noisy and long-tailed data simultaneously. Our method can extract this class-balanced subset with clean labels, which brings effective performance gains for long-tailed classification with label noise.
  arXiv  Detail & Related papers  (2024-04-10T07:34:37Z)
• Combating Label Noise With A General Surrogate Model For Sample Selection [77.45468386115306]
  We propose to leverage the vision-language surrogate model CLIP to filter noisy samples automatically. We validate the effectiveness of our proposed method on both real-world and synthetic noisy datasets.
  arXiv  Detail & Related papers  (2023-10-16T14:43:27Z)
• Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition [70.00984078351927]
  This paper focuses on reducing noise based on some inherent properties of multi-label classification and long-tailed learning under noisy cases. We propose a Stitch-Up augmentation to synthesize a cleaner sample, which directly reduces multi-label noise. A Heterogeneous Co-Learning framework is further designed to leverage the inconsistency between long-tailed and balanced distributions.
  arXiv  Detail & Related papers  (2023-07-03T09:20:28Z)
• Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation [16.283722126438125]
  Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples. Such curriculum is sub-optimal since it does not consider the actual label noise rate in the training set. This paper addresses this issue with a new noise-rate estimation method that is easily integrated with most state-of-the-art (SOTA) LNL methods.
  arXiv  Detail & Related papers  (2023-05-31T01:46:14Z)
• Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features [43.41573458276422]
  We introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network. The proposed method trains the noise detector in a supervised manner using the dataset with synthesized label noises. Results show that the proposed method precisely detects mislabeled samples on various datasets without further adaptation.
  arXiv  Detail & Related papers  (2022-12-19T09:39:30Z)
• Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling [22.62790706276081]
  Training deep neural network (DNN) with noisy labels is practically challenging. Previous efforts tend to handle part or full data in a unified denoising flow. We propose a coarse-to-fine robust learning method called CREMA to handle noisy data in a divide-and-conquer manner.
  arXiv  Detail & Related papers  (2022-08-23T02:06:38Z)
• Neighborhood Collective Estimation for Noisy Label Identification and Correction [92.20697827784426]
  Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias. We propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors.
  arXiv  Detail & Related papers  (2022-08-05T14:47:22Z)
• Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile [78.1212767880785]
  meta-learner is prone to overfitting since there are only a few available samples. When handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise. We present Eigen-Reptile (ER) that updates the meta- parameters with the main direction of historical task-specific parameters.
  arXiv  Detail & Related papers  (2022-06-04T08:48:02Z)
• Towards Robustness to Label Noise in Text Classification via Noise Modeling [7.863638253070439]
  Large datasets in NLP suffer from noisy labels, due to erroneous automatic and human annotation procedures. We study the problem of text classification with label noise, and aim to capture this noise through an auxiliary noise model over the classifier.
  arXiv  Detail & Related papers  (2021-01-27T05:41:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.