sparce-activation

A technique in neural networks that activates only a small subset of neurons for each input.

Sparse activation aims to enhance model efficiency and interpretability by ensuring that only a fraction of neurons are activated at any given time, reducing computational load. This approach can lead to improved generalization in deep learning models. A common misconception is that it compromises model performance; however, it can enhance certain tasks by focusing on relevant features.