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.