Tunable Deep

Tunable deep learning focuses on creating neural networks whose behavior can be adjusted after initial training, offering greater control and adaptability to various tasks and conditions. Current research explores architectures like tunable convolutional layers and hypernetworks generating soft prompts, aiming for improved efficiency and generalization across diverse applications, including real-time image processing and video analysis. This adaptability is crucial for addressing challenges in areas such as additive manufacturing process monitoring and improving the performance of foundation models, ultimately enhancing the practicality and robustness of deep learning systems.