Instance-Dependent Noisy Label Learning via Graphical Modelling

• URL: http://arxiv.org/abs/2209.00906v1
• Date: Fri, 2 Sep 2022 09:27:37 GMT
• Title: Instance-Dependent Noisy Label Learning via Graphical Modelling
• Authors: Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro
• Abstract summary: Noisy labels are troublesome in the ecosystem of deep learning because models can easily overfit them. We present a new graphical modelling approach called InstanceGM that combines discriminative and generative models.
• Score: 30.922188228545906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them. There are many types of label noise, such as symmetric, asymmetric and instance-dependent noise (IDN), with IDN being the only type that depends on image information. Such dependence on image information makes IDN a critical type of label noise to study, given that labelling mistakes are caused in large part by insufficient or ambiguous information about the visual classes present in images. Aiming to provide an effective technique to address IDN, we present a new graphical modelling approach called InstanceGM, that combines discriminative and generative models. The main contributions of InstanceGM are: i) the use of the continuous Bernoulli distribution to train the generative model, offering significant training advantages, and ii) the exploration of a state-of-the-art noisy-label discriminative classifier to generate clean labels from instance-dependent noisy-label samples. InstanceGM is competitive with current noisy-label learning approaches, particularly in IDN benchmarks using synthetic and real-world datasets, where our method shows better accuracy than the competitors in most experiments.

Related papers

• Inaccurate Label Distribution Learning with Dependency Noise [52.08553913094809]
  We introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learning. We show that DN-ILDL effectively addresses the ILDL problem and outperforms existing LDL methods.
  arXiv  Detail & Related papers  (2024-05-26T07:58:07Z)
• Noisy Label Processing for Classification: A Survey [2.8821062918162146]
  In the long, tedious process of data annotation, annotators are prone to make mistakes, resulting in incorrect labels of images. It is crucial to combat noisy labels for computer vision tasks, especially for classification tasks. We propose an algorithm to generate a synthetic label noise pattern guided by real-world data.
  arXiv  Detail & Related papers  (2024-04-05T15:11:09Z)
• Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances [55.37242480995541]
  We propose to denoise noisy NER data with guidance from a small set of clean instances. Along with the main NER model we train a discriminator model and use its outputs to recalibrate the sample weights. Results on public crowdsourcing and distant supervision datasets show that the proposed method can consistently improve performance with a small guidance set.
  arXiv  Detail & Related papers  (2023-10-25T17:23:37Z)
• Rethinking the Value of Labels for Instance-Dependent Label Noise Learning [43.481591776038144]
  noisy labels in real-world applications often depend on both the true label and the features. In this work, we tackle instance-dependent label noise with a novel deep generative model that avoids explicitly modeling the noise transition matrix. Our algorithm leverages casual representation learning and simultaneously identifies the high-level content and style latent factors from the data.
  arXiv  Detail & Related papers  (2023-05-10T15:29:07Z)
• 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)
• Approximating Instance-Dependent Noise via Instance-Confidence Embedding [87.65718705642819]
  Label noise in multiclass classification is a major obstacle to the deployment of learning systems. We investigate the instance-dependent noise (IDN) model and propose an efficient approximation of IDN to capture the instance-specific label corruption.
  arXiv  Detail & Related papers  (2021-03-25T02:33:30Z)
• 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)
• Attention-Aware Noisy Label Learning for Image Classification [97.26664962498887]
  Deep convolutional neural networks (CNNs) learned on large-scale labeled samples have achieved remarkable progress in computer vision. The cheapest way to obtain a large body of labeled visual data is to crawl from websites with user-supplied labels, such as Flickr. This paper proposes the attention-aware noisy label learning approach to improve the discriminative capability of the network trained on datasets with potential label noise.
  arXiv  Detail & Related papers  (2020-09-30T15:45:36Z)
• Learning from Noisy Labels with Noise Modeling Network [7.523041606515877]
  Noise Modeling Network (NMN) follows our convolutional neural network (CNN) and integrates with it. NMN learns the distribution of noise patterns directly from the noisy data. We show that the integrated NMN/CNN learning system consistently improves the classification performance.
  arXiv  Detail & Related papers  (2020-05-01T20:32:22Z)

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.