Activation Sparsity

Activation sparsity refers to the phenomenon where, in a neural network, many neurons (or units) produce little to no output for a given input, resulting in a high number of zero activations. This means that only a small subset of neurons are actively contributing to the network's decision at any moment. Sparsity enhances computational efficiency and can improve the network's ability to generalize by focusing on the most relevant features. It’s similar to only paying attention to the most important signals while ignoring irrelevant data, making processing faster and more focused.