Adaptive Knowledge Distillation

Adaptive knowledge distillation refines the process of transferring knowledge from a complex "teacher" model to a simpler "student" model, focusing on dynamically adjusting the knowledge transfer process based on the student's learning progress or data characteristics. Current research explores adaptive methods across various architectures, including vision transformers, graph neural networks, and recurrent neural networks, often incorporating techniques like adaptive loss weighting, curriculum learning, and contrastive learning to improve efficiency and accuracy. This approach is significant for improving model efficiency in resource-constrained environments and enhancing performance in challenging scenarios such as continual learning, cross-domain adaptation, and low-quality data handling, impacting fields ranging from recommendation systems to image and speech recognition.