Net2Vec shows most CNN concepts need several jointly-fit filters

Fong & Vedaldi fit each Broden-annotated concept as a learned weight vector over multiple AlexNet filter-activation maps, scored by segmentation-mask IoU [fong-vedaldi-2018-net2vec] Most concepts require several filters jointly rather than aligning with any single filter, a multi-filter subspace generalization of single-unit dissection [fong-vedaldi-2018-net2vec] Individual filters are typically polysemantic, encoding more than one concept; no causal intervention is performed [fong-vedaldi-2018-net2vec]