The self-distillation training strategy used in SDMM can be combined with other self-supervised learning objectives, such as contrastive learning, to potentially further boost the feature accuracy. By integrating self-distillation with contrastive learning, the model can benefit from both the knowledge distillation process and the feature discrimination aspect of contrastive learning.
Here's how the combination can be implemented:
Dual Objective Training: The model can be trained with a dual objective where it simultaneously minimizes the self-distillation loss and the contrastive loss. The self-distillation loss ensures that the student network learns from the teacher's knowledge, while the contrastive loss encourages the model to learn discriminative features.
Feature Space Alignment: The features learned through self-distillation can be aligned with the features learned through contrastive learning in a shared feature space. This alignment can help in leveraging the benefits of both training strategies to enhance the overall feature representation.
Regularization: The combination of self-distillation and contrastive learning can act as a regularization technique, preventing overfitting and improving the generalization ability of the model.
By combining self-distillation with contrastive learning, the model can potentially achieve higher feature accuracy and robustness, leading to improved performance in various downstream tasks.