Activation Function Synthesis

Activation Function Synthesis refers to the automated design and optimization of activation functions in neural networks, aiming to enhance the model's learning ability and generalization performance. This technique achieves effective modeling of complex nonlinear relationships through dynamic adjustment of the parameters of activation functions, thereby obtaining better performance in deep learning tasks. Its application value lies in adapting to different datasets and task requirements, improving the efficiency of model training and prediction accuracy.