Related papers: Robust Feature Learning Against Noisy Labels

Robust Feature Learning Against Noisy Labels

URL: http://arxiv.org/abs/2307.04312v1
Date: Mon, 10 Jul 2023 02:55:35 GMT
Title: Robust Feature Learning Against Noisy Labels
Authors: Tsung-Ming Tai, Yun-Jie Jhang, Wen-Jyi Hwang
Abstract summary: Mislabeled samples can significantly degrade the generalization of models. progressive self-bootstrapping is introduced to minimize the negative impact of supervision from noisy labels. Experimental results show that our proposed method can efficiently and effectively enhance model robustness under severely noisy labels.
Score: 0.2082426271304908
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the generalization of models and result in memorizing samples, further learning erroneous associations of data contents to incorrect annotations. To this end, this paper proposes an efficient approach to tackle noisy labels by learning robust feature representation based on unsupervised augmentation restoration and cluster regularization. In addition, progressive self-bootstrapping is introduced to minimize the negative impact of supervision from noisy labels. Our proposed design is generic and flexible in applying to existing classification architectures with minimal overheads. Experimental results show that our proposed method can efficiently and effectively enhance model robustness under severely noisy labels.

Related papers

ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance [53.73316938815873]
We propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE) to learn representations with error tolerance. ERASE combines prototype pseudo-labels with propagated denoised labels and updates representations with error resilience. Our method can outperform multiple baselines with clear margins in broad noise levels and enjoy great scalability.
arXiv Detail & Related papers (2023-12-13T17:59:07Z)
Analyze the Robustness of Classifiers under Label Noise [5.708964539699851]
Label noise in supervised learning, characterized by erroneous or imprecise labels, significantly impairs model performance. This research focuses on the increasingly pertinent issue of label noise's impact on practical applications.
arXiv Detail & Related papers (2023-12-12T13:51:25Z)
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking [33.87292143223425]
We propose a novel training approach termed adversarial noisy masking. It adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. It is tested on both synthetic and real-world noisy datasets.
arXiv Detail & Related papers (2023-02-14T03:13:26Z)
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning [101.86916775218403]
This paper revisits the popular pseudo-labeling methods via a unified sample weighting formulation. We propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.
arXiv Detail & Related papers (2023-01-26T03:53:25Z)
Transductive CLIP with Class-Conditional Contrastive Learning [68.51078382124331]
We propose Transductive CLIP, a novel framework for learning a classification network with noisy labels from scratch. A class-conditional contrastive learning mechanism is proposed to mitigate the reliance on pseudo labels. ensemble labels is adopted as a pseudo label updating strategy to stabilize the training of deep neural networks with noisy labels.
arXiv Detail & Related papers (2022-06-13T14:04:57Z)
Learning with Noisy Labels by Targeted Relabeling [52.0329205268734]
Crowdsourcing platforms are often used to collect datasets for training deep neural networks. We propose an approach which reserves a fraction of annotations to explicitly relabel highly probable labeling errors.
arXiv Detail & Related papers (2021-10-15T20:37:29Z)
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis [69.48582264712854]
We propose a robust learning method to perform robust visual sentiment analysis. Our method relies on an external memory to aggregate and filter noisy labels during training. We establish a benchmark for visual sentiment analysis with label noise using publicly available datasets.
arXiv Detail & Related papers (2021-09-15T18:18:28Z)
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise [27.885927200376386]
We show that open-set noisy labels can be non-toxic and even benefit the robustness against inherent noisy labels. We propose a simple yet effective regularization by introducing Open-set samples with Dynamic Noisy Labels (ODNL) into training.
arXiv Detail & Related papers (2021-06-21T07:15:50Z)
Learning from Noisy Labels for Entity-Centric Information Extraction [17.50856935207308]
We propose a simple co-regularization framework for entity-centric information extraction. These models are jointly optimized with task-specific loss, and are regularized to generate similar predictions. In the end, we can take any of the trained models for inference.
arXiv Detail & Related papers (2021-04-17T22:49:12Z)
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)

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