Extracting a Clean and Balanced Subset from Noisy Long-tailed Datasets for Robust Classification

Conceitos Básicos

The core message of this work is to effectively extract a clean and class-balanced subset from a noisy and long-tailed training dataset, which can be used to train a robust classification model.

Resumo

The article addresses the joint issue of long-tailed distribution and label noise in real-world datasets, which poses significant challenges for training robust classification models.

Key highlights:

• Real-world datasets often exhibit class imbalance and label noise, which can severely degrade model performance.
• Existing methods typically focus on either long-tailed or noisy label problems, but cannot effectively handle the joint issue.
• The authors propose a novel pseudo-labeling framework that leverages class prototypes and optimal transport to simultaneously mitigate the effects of imbalance and noise.
• The method first computes the optimal transport distance between sample representations and class prototypes, using a class-balanced prototype distribution. This allows estimating balanced pseudo-labels for the training samples.
• The authors then introduce a simple filtering criterion to extract a clean and less imbalanced subset from the original training data, based on the observed labels and estimated pseudo-labels.
• Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in addressing noisy long-tailed classification problems.

Estatísticas

The number of samples in majority classes is much larger than that in minority classes, leading to an imbalanced dataset.
Part of the training dataset is corrupted by noisy labels, where the observed labels do not match the ground-truth labels.

Citações

"Real-world datasets usually are class-imbalanced and corrupted by label noise. To solve the joint issue of long-tailed distribution and label noise, most previous works usually aim to design a noise detector to distinguish the noisy and clean samples."
"When the training dataset follows a long-tailed label distribution while contains label noise, training a robust model is even more challenging."

Principais Insights Extraídos De

Extracting Clean and Balanced Subset for Noisy Long-tailed Classification

by Zhuo Li,He Z... às arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06795.pdf

Perguntas Mais Profundas

How can the proposed pseudo-labeling framework be extended to handle other types of noisy distributions beyond the long-tailed setting, such as multi-modal or clustered noise patterns?

The proposed pseudo-labeling framework can be extended to handle other types of noisy distributions by adapting the distribution matching approach to accommodate different noise patterns. For multi-modal noise, where the noise is distributed across multiple modes or clusters, the framework can be modified to incorporate multiple sets of prototypes representing different modes or clusters. By optimizing the OT distance between the sample distribution and multiple prototype distributions, the framework can effectively pseudo-label samples based on their proximity to different modes or clusters. This approach allows for the identification and handling of samples affected by various noise patterns present in the data.

What are the potential limitations of the current filtering criteria based on the observed labels and estimated pseudo-labels, and how could it be further improved?

One potential limitation of the current filtering criteria based on observed labels and estimated pseudo-labels is its reliance on the assumption that observed labels and pseudo-labels are imperfect but effective approximations of the ground truth. This assumption may not always hold true, especially in cases where the noise level is high or the labeling errors are systematic. To improve the filtering criteria, additional measures can be incorporated to enhance the reliability of the filtering process. For example, introducing a confidence score based on the consistency between observed labels, pseudo-labels, and model predictions can help identify and filter out samples with uncertain or conflicting labels. Additionally, incorporating uncertainty estimation techniques or ensemble methods to assess the reliability of pseudo-labels can further enhance the filtering criteria's robustness.

Can the insights from this work on jointly addressing imbalance and noise be applied to other machine learning tasks beyond image classification, such as natural language processing or speech recognition?

Yes, the insights from this work on jointly addressing imbalance and noise in image classification can be applied to other machine learning tasks, such as natural language processing (NLP) or speech recognition. In NLP tasks, where imbalanced datasets and noisy labels are common challenges, the proposed pseudo-labeling framework can be adapted to handle text data by representing samples as embeddings and class prototypes as centroids in the embedding space. By optimizing the OT distance between the sample distribution and prototype distribution, the framework can effectively pseudo-label text samples and mitigate the impact of noisy labels and class imbalance. Similarly, in speech recognition tasks, the framework can be applied to address noisy audio data and imbalanced label distributions by leveraging representations of audio samples and prototype representations of different speech classes. By extending the distribution matching approach to these domains, the framework can enhance model training and improve performance in NLP and speech recognition tasks.