Figure 2: Overview of RwF. Filter localization (left): parameter-space integrated gradients rank
layers, then filters within them, and the selected set is expanded with high-IG, low-similarity filters.
Filter learning (right): the localized filters are regenerated from a filter bank by factorized matrices
with cross-task attention and assembled back into the frozen backbone.