Yes, the insights gained from this work on improving algorithm, model, and data efficiency can be applied to other self-supervised learning paradigms beyond instance discrimination.
For algorithm efficiency, the strategies for enhancing training efficiency, such as feature calibration and gradient updates, can be adapted to different self-supervised learning tasks. By optimizing the update rules and initialization methods, the training process can be streamlined and accelerated for various self-supervised learning paradigms.
In terms of model efficiency, the focus on improving performance with small models and limited data can be beneficial for other self-supervised learning approaches. Techniques for knowledge distillation, model compression, and hyperparameter tuning can be applied to enhance the efficiency and effectiveness of models in different self-supervised learning contexts.
Regarding data efficiency, the exploration of performance under different scales of training data and the study of data-efficient training methods can be valuable for a wide range of self-supervised learning paradigms. Understanding how to achieve optimal results with limited data and resources is crucial for the practical application of self-supervised learning across various domains and tasks.