Single Deep

Single deep learning models are being explored as a means to improve efficiency and generalization across diverse machine learning tasks, moving away from ensembles of separate networks. Current research focuses on developing architectures capable of handling multiple tasks or domains simultaneously, often employing transformer-based models or incorporating techniques like knowledge distillation and heterogeneous quantization to optimize performance and resource utilization. This approach promises to reduce computational costs and improve the adaptability of deep learning systems for various applications, including image processing, material science, and real-time systems.