Single Model

Single-model approaches in machine learning aim to perform multiple tasks or handle diverse data modalities using a single, unified architecture, thereby reducing computational complexity and maintenance costs compared to using multiple specialized models. Current research focuses on developing single models capable of handling diverse data types (e.g., images, videos, 3D point clouds, text) and tasks (e.g., object detection, segmentation, translation, uncertainty quantification), often leveraging transformer-based architectures and techniques like knowledge distillation or adaptive weight assignment. This trend is significant because it improves efficiency and scalability, enabling the deployment of powerful AI systems in resource-constrained environments and facilitating the development of more generalizable and robust AI solutions across various applications.