The field of deep learning is moving towards more efficient and robust models, with a focus on finetuning and transfer learning. Recent research has highlighted the importance of image quality and its impact on model performance, as well as the need for adaptability in different scenarios and hardware constraints. Innovative approaches such as task-specific learning adaptation and evolutionary selective fine-tuning have shown promising results in improving model efficiency and accuracy. Noteworthy papers include:

• Impact of Clinical Image Quality on Efficient Foundation Model Finetuning, which investigates the effect of image quality on finetuning and highlights the need for quality standards in finetuning data.
• TSLA: A Task-Specific Learning Adaptation for Semantic Segmentation on Autonomous Vehicles Platform, which implements a dynamic adaptability mechanism to customize semantic segmentation networks according to computing power and specific scenarios.
• A Guide to Robust Generalization, which presents a comprehensive benchmark of robust fine-tuning and offers practical guidance on design choices for robust generalization.
• Transfer learning optimization based on evolutionary selective fine tuning, which introduces an evolutionary adaptive fine-tuning technique to enhance transfer learning efficiency.