PackNet: Capacity Packing for Continual Learning

PackNet solves catastrophic forgetting by structural guarantee rather than by hoping regularization holds: it iteratively prunes a fixed-size network, freezing the most important surviving weights into a permanent binary mask for each task and handing the rest of the capacity forward to the next one. This simulator renders that process as a 16×16 grid of neural weights in 3D — each "Train Next Task" pass trains the free slots, ranks them by simulated importance, and freezes the top fraction (set by the Keep ratio slider) into a new colored, permanently frozen layer. Because frozen weights are never touched again, each task's accuracy — modeled as a saturating function of how many weights it was allocated relative to its difficulty — is locked in forever once its mask is cut, and the live per-task accuracy list proves it: earlier tasks never regress as later ones are added, right up until the shrinking free pool runs out.