Self Distilled Self Supervised

Self-distilled self-supervised learning aims to improve the efficiency and performance of self-supervised models by leveraging knowledge distillation techniques. Current research focuses on applying this approach to various modalities, including speech and vision, often employing transformer-based architectures and exploring different distillation strategies like ensemble methods and dual-view cross-correlation. This approach is significant because it allows for the creation of smaller, faster models that retain the performance of larger, computationally expensive counterparts, particularly beneficial for resource-constrained applications like on-device processing. The resulting improved efficiency and performance have implications across numerous fields, including speech recognition, speaker verification, and medical image analysis.