Single Task

Single-task learning, despite the rise of multi-task approaches, remains a significant area of research focusing on optimizing the performance of individual models for specific tasks. Current efforts explore efficient model architectures and algorithms, including transformers, ResNets, and diffusion models, often incorporating techniques like regularization and attention mechanisms to enhance performance and address issues like catastrophic forgetting in continual learning scenarios. These advancements are crucial for resource-constrained environments and situations where specialized models are preferred, impacting various fields from healthcare (medical image processing) to natural language processing (question answering). The ongoing research emphasizes improving single-task model efficiency and generalization, even within the context of multi-task learning frameworks.