Teacher Student Transformer

Teacher-student transformers leverage knowledge distillation to improve the efficiency and performance of transformer-based models, primarily focusing on reducing computational costs and enhancing generalization across diverse tasks and domains. Current research explores techniques like selective attention layer removal, embedding compression, and cross-architecture distillation (e.g., between transformers and CNNs) to achieve these goals, often employing ensemble methods and self-supervised learning strategies. This approach holds significant promise for deploying computationally expensive transformer models on resource-constrained devices and improving their performance on data-scarce tasks, impacting various fields including computer vision, audio processing, and object detection.