Adaptive Activation

Adaptive activation research focuses on enhancing the performance and efficiency of neural networks by dynamically adjusting activation functions based on data characteristics or hardware constraints. Current efforts explore novel activation functions with adaptable parameters, investigating their application in various network architectures, including spiking neural networks, and optimizing their integration with quantization techniques for resource-constrained environments. This work aims to improve model accuracy, energy efficiency, and adaptability to diverse datasets and hardware platforms, impacting fields like remote sensing, image recognition, and speech processing.