Model Reuse

Model reuse in machine learning focuses on leveraging pre-trained models to reduce training costs and improve efficiency for new tasks. Current research explores diverse approaches, including repurposing model components for specific applications (like recommender systems), developing methods for detecting unauthorized model reuse, and employing techniques like self-supervised learning and multi-linear operators to enhance transferability and efficiency across different architectures. This field is significant because it addresses the high computational cost of training large models, enabling faster development and deployment of AI systems across various domains, from computer-aided design to medical image analysis.