Net Sparsity

Net sparsity refers to the proportion of zero or nearly-zero elements within a network's parameters, such as weights in a neural network. A high net sparsity indicates that many connections are inactive or negligible, which can make the model more efficient and easier to interpret. Essentially, it measures how "simplified" or "compact" the network is, often leading to faster computations and reduced memory usage without significantly sacrificing performance. Think of it as a network with many "empty" or inactive pathways, streamlining the overall structure.