insight - Machine Learning - # Divergence-Based Empirical Risk Functions and Regularizers for Semi-Supervised Learning

Robust Semi-Supervised Learning via Divergence-Based Empirical Risk Functions and Regularizers

Q: How can the proposed divergence-based empirical risk functions and regularizers be extended to other semi-supervised learning techniques beyond pseudo-labeling and entropy minimization

The proposed divergence-based empirical risk functions and regularizers can be extended to other semi-supervised learning techniques by incorporating them into frameworks that involve a combination of self-training methods and consistency regularization. For example, methods like FixMatch and MixMatch already leverage consistency regularization to improve model performance in semi-supervised learning. By integrating the divergence-based empirical risk functions as additional regularization terms in these frameworks, we can potentially enhance the robustness and generalization capabilities of the models. Furthermore, these divergence-based approaches can also be applied in conjunction with techniques like Mean Teacher, where an ensemble of models is used to provide consistent predictions. By incorporating the divergence-based empirical risk functions into the loss functions of both the student and teacher models, we can potentially improve the quality of the pseudo-labels generated during the training process.

Q: What are the theoretical guarantees on the performance of the divergence-based empirical risk functions compared to traditional approaches in the presence of label noise or distribution shift

Theoretical guarantees on the performance of the proposed divergence-based empirical risk functions compared to traditional approaches in the presence of label noise or distribution shift can be analyzed based on the properties of the chosen divergences. For instance, f-divergences and α-Rényi divergences have specific characteristics that make them suitable for handling noisy labels and distribution shifts. Robustness to Noisy Labels: The divergence-based empirical risk functions are designed to be more robust to noisy pseudo-labels, where the pseudo-labels may differ from the true labels of the unlabeled data samples. The theoretical guarantees stem from the properties of f-divergences and α-Rényi divergences, which enable the models to learn from noisy data more effectively. Upper Bounds on True Risk: By providing an upper bound on the true risk of the empirical risk functions under certain conditions, the theoretical guarantees ensure that the models trained using these divergence-based approaches have a performance ceiling that is quantifiable. This can help in understanding the limitations and capabilities of the models in semi-supervised learning scenarios. Comparison with Traditional Approaches: The theoretical analysis can demonstrate the superiority of the divergence-based empirical risk functions over traditional self-training methods in terms of robustness, convergence properties, and generalization performance. These guarantees provide insights into the effectiveness of the proposed approaches in challenging learning scenarios.

Q: Can the insights from this work be applied to develop robust loss functions for supervised learning tasks with noisy labels

The insights from this work can indeed be applied to develop robust loss functions for supervised learning tasks with noisy labels. By leveraging the principles of f-divergences and α-Rényi divergences, we can design loss functions that are more resilient to label noise and distribution shifts in the training data. Robust Loss Functions: The properties of f-divergences and α-Rényi divergences, such as their ability to quantify the dissimilarity between probability distributions, can be utilized to design loss functions that penalize model predictions that deviate significantly from the ground truth labels. These robust loss functions can help mitigate the impact of noisy labels on the training process. Regularization Techniques: The regularization techniques inspired by divergences, such as entropy minimization and pseudo-labeling, can be adapted for supervised learning tasks with noisy labels. By incorporating these techniques into the loss functions of supervised models, we can improve the model's ability to learn from imperfectly labeled data. Generalization to Supervised Learning: The theoretical foundations and insights gained from applying divergence-based approaches in semi-supervised learning can be extended to supervised learning settings. By considering the challenges posed by noisy labels in supervised tasks, we can develop loss functions that enhance the model's robustness and generalization performance in the presence of label noise.

Core Concepts

This paper proposes novel empirical risk functions and regularizers inspired by f-divergences and α-Rényi divergence for self-training methods in semi-supervised learning, such as pseudo-labeling and entropy minimization. These divergence-based approaches are more robust to noisy pseudo-labels compared to traditional self-training methods.

Abstract

The paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning. These approaches draw inspiration from various divergence measures, such as f-divergences and α-Rényi divergences.

The key highlights and insights are:

Proposed novel empirical risk functions (DERs) based on f-divergences and α-Rényi divergence for supervised learning applications.
Adapted the DERs for semi-supervised learning (SSL) scenarios, specifically for pseudo-labeling and entropy minimization techniques.
Provided an upper bound on the true risk of some DERs under the fully supervised learning (FSL) scenario, when the divergence is a metric distance.
Demonstrated the robustness of the proposed DERs to noisy pseudo-labels generated by self-training methods, compared to traditional approaches.
Introduced two algorithms, DP-SSL and DEM-SSL, that leverage the proposed DERs for pseudo-labeling and entropy minimization in SSL, respectively.
Empirical analysis showed that the DERs, particularly JS-ERM, outperform traditional self-training methods in the presence of noisy pseudo-labels.

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Stats

The paper does not provide any specific numerical data or statistics. It focuses on the theoretical development of divergence-based empirical risk functions and their application to semi-supervised learning.

Quotes

"Our empirical risk functions are more robust to noisy pseudo-labels (i.e., the pseudo-label is different from the true label) of unlabeled data samples, which are generated by self-training approaches."
"Inspired by some divergences properties, we also provide an upper bound on the true risk of some empirical risk functions."

Key Insights Distilled From

Robust Semi-supervised Learning via $f$-Divergence and $α$-Rényi Divergence

by Gholamali Am... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00454.pdf

Robust Semi-supervised Learning via $f$-Divergence and $α$-Rényi Divergence

Deeper Inquiries

How can the proposed divergence-based empirical risk functions and regularizers be extended to other semi-supervised learning techniques beyond pseudo-labeling and entropy minimization

The proposed divergence-based empirical risk functions and regularizers can be extended to other semi-supervised learning techniques by incorporating them into frameworks that involve a combination of self-training methods and consistency regularization. For example, methods like FixMatch and MixMatch already leverage consistency regularization to improve model performance in semi-supervised learning. By integrating the divergence-based empirical risk functions as additional regularization terms in these frameworks, we can potentially enhance the robustness and generalization capabilities of the models.
Furthermore, these divergence-based approaches can also be applied in conjunction with techniques like Mean Teacher, where an ensemble of models is used to provide consistent predictions. By incorporating the divergence-based empirical risk functions into the loss functions of both the student and teacher models, we can potentially improve the quality of the pseudo-labels generated during the training process.

What are the theoretical guarantees on the performance of the divergence-based empirical risk functions compared to traditional approaches in the presence of label noise or distribution shift

Theoretical guarantees on the performance of the proposed divergence-based empirical risk functions compared to traditional approaches in the presence of label noise or distribution shift can be analyzed based on the properties of the chosen divergences. For instance, f-divergences and α-Rényi divergences have specific characteristics that make them suitable for handling noisy labels and distribution shifts.

Robustness to Noisy Labels: The divergence-based empirical risk functions are designed to be more robust to noisy pseudo-labels, where the pseudo-labels may differ from the true labels of the unlabeled data samples. The theoretical guarantees stem from the properties of f-divergences and α-Rényi divergences, which enable the models to learn from noisy data more effectively.

Upper Bounds on True Risk: By providing an upper bound on the true risk of the empirical risk functions under certain conditions, the theoretical guarantees ensure that the models trained using these divergence-based approaches have a performance ceiling that is quantifiable. This can help in understanding the limitations and capabilities of the models in semi-supervised learning scenarios.

Comparison with Traditional Approaches: The theoretical analysis can demonstrate the superiority of the divergence-based empirical risk functions over traditional self-training methods in terms of robustness, convergence properties, and generalization performance. These guarantees provide insights into the effectiveness of the proposed approaches in challenging learning scenarios.

Can the insights from this work be applied to develop robust loss functions for supervised learning tasks with noisy labels

The insights from this work can indeed be applied to develop robust loss functions for supervised learning tasks with noisy labels. By leveraging the principles of f-divergences and α-Rényi divergences, we can design loss functions that are more resilient to label noise and distribution shifts in the training data.

Robust Loss Functions: The properties of f-divergences and α-Rényi divergences, such as their ability to quantify the dissimilarity between probability distributions, can be utilized to design loss functions that penalize model predictions that deviate significantly from the ground truth labels. These robust loss functions can help mitigate the impact of noisy labels on the training process.

Regularization Techniques: The regularization techniques inspired by divergences, such as entropy minimization and pseudo-labeling, can be adapted for supervised learning tasks with noisy labels. By incorporating these techniques into the loss functions of supervised models, we can improve the model's ability to learn from imperfectly labeled data.

Generalization to Supervised Learning: The theoretical foundations and insights gained from applying divergence-based approaches in semi-supervised learning can be extended to supervised learning settings. By considering the challenges posed by noisy labels in supervised tasks, we can develop loss functions that enhance the model's robustness and generalization performance in the presence of label noise.