Knowledge Amalgamation

Knowledge amalgamation (KA) focuses on efficiently combining the knowledge embedded within multiple pre-trained models (teachers) to create a more versatile and compact student model, reducing the need for extensive retraining. Current research explores KA across various model architectures, including convolutional neural networks and transformers, employing techniques like contrastive learning, self-regulation, and selective aggregation to effectively integrate teacher knowledge. This field is significant because it addresses the challenges of data scarcity, model redundancy, and computational cost in machine learning, offering potential for improved efficiency and resource utilization in diverse applications. The development of robust KA methods promises to accelerate progress in various domains by leveraging existing models more effectively.