On the Predictability of Pruning Across Scales

Understand

We show that the error of iteratively magnitude-pruned networks empirically follows a scaling law with interpretable coefficients that depend on the architecture and task.

• We functionally approximate the error of the pruned networks, showing it is predictable in terms of an invariant tying width, depth, and pruning level, such that networks of vastly different pruned densities are interchangeable.
• We demonstrate the accuracy of this approximation over orders of magnitude in depth, width, dataset size, and density.
• We show that the functional form holds (generalizes) for large scale data (e.g., ImageNet) and architectures (e.g., ResNets).