The Exploration of Error Bounds in Classification with Noisy Labels

• URL: http://arxiv.org/abs/2501.15163v2
• Date: Thu, 19 Jun 2025 12:48:37 GMT
• Title: The Exploration of Error Bounds in Classification with Noisy Labels
• Authors: Haixia Liu, Boxiao Li, Can Yang, Yang Wang,
• Abstract summary: In this paper, we focus on the error bounds of excess risks for classification problems with noisy labels within deep learning frameworks.<n>We derive error bounds for the excess risk, decomposing it into statistical error and approximation error.<n>Under the low-dimensional manifold hypothesis, we further refine the approximation error to mitigate the impact of high-dimensional input spaces.
• Score: 7.657250843344973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerous studies have shown that label noise can lead to poor generalization performance, negatively affecting classification accuracy. Therefore, understanding the effectiveness of classifiers trained using deep neural networks in the presence of noisy labels is of considerable practical significance. In this paper, we focus on the error bounds of excess risks for classification problems with noisy labels within deep learning frameworks. We derive error bounds for the excess risk, decomposing it into statistical error and approximation error. To handle statistical dependencies (e.g., mixing sequences), we employ an independent block construction to bound the error, leveraging techniques for dependent processes. For the approximation error, we establish these theoretical results to the vector-valued setting, where the output space consists of $K$-dimensional unit vectors. Finally, under the low-dimensional manifold hypothesis, we further refine the approximation error to mitigate the impact of high-dimensional input spaces.

Related papers

• Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts [4.795811957412855]
  Noise in data appears to be inevitable in most real-world machine learning applications. We investigate the less explored area of noisy label learning for multilabel classifications. Our model posits that label noise arises from a shift in the latent variable, providing a more robust and beneficial means for noisy learning.
  arXiv  Detail & Related papers  (2025-02-20T05:41:52Z)
• Classification Error Bound for Low Bayes Error Conditions in Machine Learning [50.25063912757367]
  We study the relationship between the error mismatch and the Kullback-Leibler divergence in machine learning.<n>Motivated by recent observations of low model-based classification errors in many machine learning tasks, we propose a linear approximation of the classification error bound for low Bayes error conditions.
  arXiv  Detail & Related papers  (2025-01-27T11:57:21Z)
• An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes [46.663081214928226]
  We propose an unbiased risk estimator with theoretical guarantees for PLLAC. We provide a theoretical analysis of the estimation error bound of PLLAC. Experiments on benchmark, UCI and real-world datasets demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-09-29T07:36:16Z)
• Probabilistic Safety Regions Via Finite Families of Scalable Classifiers [2.431537995108158]
  Supervised classification recognizes patterns in the data to separate classes of behaviours. Canonical solutions contain misclassification errors that are intrinsic to the numerical approximating nature of machine learning. We introduce the concept of probabilistic safety region to describe a subset of the input space in which the number of misclassified instances is probabilistically controlled.
  arXiv  Detail & Related papers  (2023-09-08T22:40:19Z)
• Robust Online Classification: From Estimation to Denoising [14.535583931446807]
  We study online classification of features into labels with general hypothesis classes. Predictions are made using observed noisy labels and noiseless features. The performance is measured via minimax risk when comparing against true labels.
  arXiv  Detail & Related papers  (2023-09-04T16:17:39Z)
• Efficient Transfer Learning via Causal Bounds [8.981637739384674]
  We analyze how causal side-information accelerates online learning, and experiments on data reduction.<n>Our analysis precisely characterizes when how causal side-information accelerates online learning, and experiments on data reduction.
  arXiv  Detail & Related papers  (2023-08-07T13:24:50Z)
• Label Noise: Correcting the Forward-Correction [0.0]
  Training neural network classifiers on datasets with label noise poses a risk of overfitting them to the noisy labels. We propose an approach to tackling overfitting caused by label noise. Motivated by this observation, we propose imposing a lower bound on the training loss to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-07-24T19:41:19Z)
• Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning [0.0]
  Large annotated datasets inevitably contain noisy labels, which poses a major challenge for training deep neural networks as they easily memorize the labels.<n>Noise-robust loss functions have emerged as a notable strategy to counteract this issue, but it remains challenging to create a robust loss function which is not susceptible to underfitting.<n>We propose a novel method denoted as logit bias, which adds a real number $epsilon$ to the logit at the position of the correct class.
  arXiv  Detail & Related papers  (2023-06-08T18:38:55Z)
• Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels [61.97359362447732]
  Learning from noisy labels is an important and long-standing problem in machine learning for real applications. In this paper, we reformulate the label-noise problem from a generative-model perspective. Our model achieves new state-of-the-art (SOTA) results on all the standard real-world benchmark datasets.
  arXiv  Detail & Related papers  (2023-05-31T03:01:36Z)
• Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise [62.997667081978825]
  In high-risk environments, deep learning models need to be able to judge their uncertainty and reject inputs when there is a significant chance of misclassification. We conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole Slide Images. We observe that ensembles of methods generally lead to better uncertainty estimates as well as an increased robustness towards domain shifts and label noise.
  arXiv  Detail & Related papers  (2023-01-03T11:34:36Z)
• Learning from Multiple Unlabeled Datasets with Partial Risk Regularization [80.54710259664698]
  In this paper, we aim to learn an accurate classifier without any class labels. We first derive an unbiased estimator of the classification risk that can be estimated from the given unlabeled sets. We then find that the classifier obtained as such tends to cause overfitting as its empirical risks go negative during training. Experiments demonstrate that our method effectively mitigates overfitting and outperforms state-of-the-art methods for learning from multiple unlabeled sets.
  arXiv  Detail & Related papers  (2022-07-04T16:22:44Z)
• Prototype-Anchored Learning for Learning with Imperfect Annotations [83.7763875464011]
  It is challenging to learn unbiased classification models from imperfectly annotated datasets. We propose a prototype-anchored learning (PAL) method, which can be easily incorporated into various learning-based classification schemes. We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-06-23T10:25:37Z)
• Robustness and reliability when training with noisy labels [12.688634089849023]
  Labelling of data for supervised learning can be costly and time-consuming. Deep neural networks have proved capable of fitting random labels, regularisation and the use of robust loss functions.
  arXiv  Detail & Related papers  (2021-10-07T10:30:20Z)
• Instance-dependent Label-noise Learning under a Structural Causal Model [92.76400590283448]
  Label noise will degenerate the performance of deep learning algorithms. By leveraging a structural causal model, we propose a novel generative approach for instance-dependent label-noise learning.
  arXiv  Detail & Related papers  (2021-09-07T10:42:54Z)
• Robust Long-Tailed Learning under Label Noise [50.00837134041317]
  This work investigates the label noise problem under long-tailed label distribution. We propose a robust framework,algo, that realizes noise detection for long-tailed learning. Our framework can naturally leverage semi-supervised learning algorithms to further improve the generalisation.
  arXiv  Detail & Related papers  (2021-08-26T03:45:00Z)
• RATT: Leveraging Unlabeled Data to Guarantee Generalization [96.08979093738024]
  We introduce a method that leverages unlabeled data to produce generalization bounds. We prove that our bound is valid for 0-1 empirical risk minimization. This work provides practitioners with an option for certifying the generalization of deep nets even when unseen labeled data is unavailable.
  arXiv  Detail & Related papers  (2021-05-01T17:05:29Z)
• Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model [80.91927573604438]
  This paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness.
  arXiv  Detail & Related papers  (2021-01-14T05:43:51Z)
• A Second-Order Approach to Learning with Instance-Dependent Label Noise [58.555527517928596]
  The presence of label noise often misleads the training of deep neural networks. We show that the errors in human-annotated labels are more likely to be dependent on the difficulty levels of tasks.
  arXiv  Detail & Related papers  (2020-12-22T06:36:58Z)
• When Optimizing $f$-divergence is Robust with Label Noise [10.452709936265274]
  We show when maximizing a properly defined $f$-divergence measure with respect to a classifier's predictions and the supervised labels is robust with label noise. We derive a nice decoupling property for a family of $f$-divergence measures when label noise presents, where the divergence is shown to be a linear combination of the variational difference defined on the clean distribution and a bias term introduced due to the noise.
  arXiv  Detail & Related papers  (2020-11-07T04:31:33Z)
• Progressive Identification of True Labels for Partial-Label Learning [112.94467491335611]
  Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. This paper proposes a novel framework of classifier with flexibility on the model and optimization algorithm.
  arXiv  Detail & Related papers  (2020-02-19T08:35:15Z)

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.