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.