Deep Learning Classification with Noisy Labels

Sanchez, Guillaume; Guis, V.; Marxer, Ricard; Bouchara, Frédéric

doi:10.1109/icmew46912.2020.9105992

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Moreover, it has been confirmed that noise issues severely degrade the general performance of deep learning algorithms in classification domains [22][23][24][25][26][27]. Therefore, the issue of noisy labels continues to be studied using deep machine learning.…”

Section: Introductionmentioning

confidence: 99%

Automatically Correcting Noisy Labels for Improving Quality of Training Set in Domain-specific Sentiment Classification

Khamket

Polpinij

2022

Curr. Appl. Sci. Technol.

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Classification model performance can be degraded by label noise in the training set. The sentiment classification domain also struggles with this issue, whereby customer reviews can be mislabeled. Some customers give a rating score for a product or service that is inconsistent with the review content. If business owners are only interested in the overall rating picture that includes mislabeling, this can lead to erroneous business decisions. Therefore, this issue became the main challenge of this study. If we assume that customer reviews with noisy labels in the training data are validated and corrected before the learning process, then the training set can generate a predictive model that returns a better result for the sentiment analysis or classification process. Therefore, we proposed a mechanism, called polarity label analyzer, to improve the quality of a training set with noisy labels before the learning process. The proposed polarity label analyzer was used to assign the polarity class of each sentence in a customer review, and then polarity class of that customer review was concluded by voting. In our experiment, datasets were downloaded from TripAdvisor and two linguistic experts helped to assign the correct labels of customer reviews as the ground truth. Sentiment classifiers were developed using the k-NN, Logistic Regression, XGBoost, Linear SVM and CNN algorithms. After comparing the results of the sentiment classifiers without training set improvement and the results with training set improvement, our proposed method improved the average scores of F1 and accuracy by 20.59%.

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Section: Introductionmentioning

confidence: 99%

Automatically Correcting Noisy Labels for Improving Quality of Training Set in Domain-specific Sentiment Classification

Khamket

Polpinij

2022

Curr. Appl. Sci. Technol.

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“…This knowledge can be used to improve the current methods that learn with noisy labels [18]. However, to the best of our knowledge, most of the works studied up to now have mainly focused on examining the performance of the deep learning model under the influence of homoge-neous noisy labels imposed by corrupting all the true labels with same degree [4,27].…”

Section: Introductionmentioning

confidence: 99%

How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?

Khanal¹,

Kanan²

2021

Preprint

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Incorrectly labeled examples, or label noise, is common in real-world computer vision datasets. While the impact of label noise on learning in deep neural networks has been studied in prior work, these studies have exclusively focused on homogeneous label noise, i.e., the degree of label noise is the same across all categories. However, in the realworld, label noise is often heterogeneous, with some categories being affected to a greater extent than others. Here, we address this gap in the literature. We hypothesized that heterogeneous label noise would only affect the classes that had label noise unless there was transfer from those classes to the classes without label noise. To test this hypothesis, we designed a series of computer vision studies using MNIST, CIFAR-10, CIFAR-100, and MS-COCO where we imposed heterogeneous label noise during the training of multi-class, multi-task, and multi-label systems. Our results provide evidence in support of our hypothesis: label noise only affects the class affected by it unless there is transfer.

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How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?

Khanal

Kanan

2021

Advances in Visual Computing

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Deep Learning Classification with Noisy Labels

Cited by 4 publications

References 16 publications

Automatically Correcting Noisy Labels for Improving Quality of Training Set in Domain-specific Sentiment Classification

Automatically Correcting Noisy Labels for Improving Quality of Training Set in Domain-specific Sentiment Classification

How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?

How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?

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