Dirichlet-Based Prediction Calibration for Learning with Noisy Labels

• URL: http://arxiv.org/abs/2401.07062v1
• Date: Sat, 13 Jan 2024 12:33:04 GMT
• Title: Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
• Authors: Chen-Chen Zong, Ye-Wen Wang, Ming-Kun Xie, Sheng-Jun Huang
• Abstract summary: Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs) Existing approaches address this issue through loss correction or example selection methods. We propose the textitDirichlet-based Prediction (DPC) method as a solution.
• Score: 40.78497779769083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs). Existing approaches address this issue through loss correction or example selection methods. However, these methods often rely on the model's predictions obtained from the softmax function, which can be over-confident and unreliable. In this study, we identify the translation invariance of the softmax function as the underlying cause of this problem and propose the \textit{Dirichlet-based Prediction Calibration} (DPC) method as a solution. Our method introduces a calibrated softmax function that breaks the translation invariance by incorporating a suitable constant in the exponent term, enabling more reliable model predictions. To ensure stable model training, we leverage a Dirichlet distribution to assign probabilities to predicted labels and introduce a novel evidence deep learning (EDL) loss. The proposed loss function encourages positive and sufficiently large logits for the given label, while penalizing negative and small logits for other labels, leading to more distinct logits and facilitating better example selection based on a large-margin criterion. Through extensive experiments on diverse benchmark datasets, we demonstrate that DPC achieves state-of-the-art performance. The code is available at https://github.com/chenchenzong/DPC.

Related papers

• Deep Active Learning with Manifold-preserving Trajectory Sampling [2.0717982775472206]
  Active learning (AL) is for optimizing the selection of unlabeled data for annotation (labeling) Existing deep AL methods arguably suffer from bias incurred by clabeled data, which takes a much lower percentage than unlabeled data in AL context. We propose a novel method, namely Manifold-Preserving Trajectory Sampling (MPTS), aiming to enforce the feature space learned from labeled data to represent a more accurate manifold.
  arXiv  Detail & Related papers  (2024-10-21T03:04:09Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• 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)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• Variational Classification [51.2541371924591]
  We derive a variational objective to train the model, analogous to the evidence lower bound (ELBO) used to train variational auto-encoders. Treating inputs to the softmax layer as samples of a latent variable, our abstracted perspective reveals a potential inconsistency. We induce a chosen latent distribution, instead of the implicit assumption found in a standard softmax layer.
  arXiv  Detail & Related papers  (2023-05-17T17:47:19Z)
• Optimizing Diffusion Rate and Label Reliability in a Graph-Based Semi-supervised Classifier [2.4366811507669124]
  The Local and Global Consistency (LGC) algorithm is one of the most well-known graph-based semi-supervised (GSSL) classifiers. We discuss how removing the self-influence of a labeled instance may be beneficial, and how it relates to leave-one-out error. Within this framework, we propose methods to estimate label reliability and diffusion rate.
  arXiv  Detail & Related papers  (2022-01-10T16:58:52Z)
• The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration [21.63888208442176]
  In spite of the dominant performances of deep neural networks, recent works have shown that they are poorly calibrated. We provide a unifying constrained-optimization perspective of current state-of-the-art calibration losses. We propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances.
  arXiv  Detail & Related papers  (2021-11-30T14:21:47Z)
• Dash: Semi-Supervised Learning with Dynamic Thresholding [72.74339790209531]
  We propose a semi-supervised learning (SSL) approach that uses unlabeled examples to train models. Our proposed approach, Dash, enjoys its adaptivity in terms of unlabeled data selection.
  arXiv  Detail & Related papers  (2021-09-01T23:52:29Z)
• Being Bayesian about Categorical Probability [6.875312133832079]
  We consider a random variable of a categorical probability over class labels. In this framework, the prior distribution explicitly models the presumed noise inherent in the observed label. Our method can be implemented as a plug-and-play loss function with negligible computational overhead.
  arXiv  Detail & Related papers  (2020-02-19T02:35:32Z)

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.