  The first layer of a deep CNN backbone applies filters to an image to extract
the basic features available to later layers. During training, some filters may
go inactive, mean ing all weights in the filter approach zero. An inactive fil
ter in the final model represents a missed opportunity to extract a useful
feature. This phenomenon is especially prevalent in specialized CNNs such as
for face recogni tion (as opposed to, e.g., ImageNet). For example, in one the
most widely face recognition model (ArcFace), about half of the convolution
filters in the first layer are inactive. We propose a novel approach designed
and tested specif ically for face recognition networks, known as "CRAFT:
Contextual Re-Activation of Filters for Face Recognition Training". CRAFT
identifies inactive filters during training and reinitializes them based on the
context of strong filters at that stage in training. We show that CRAFT reduces
fraction of inactive filters from 44% to 32% on average and discovers filter
patterns not found by standard training. Compared to standard training without
reactivation, CRAFT demonstrates enhanced model accuracy on standard
face-recognition benchmark datasets including AgeDB-30, CPLFW, LFW, CALFW, and
CFP-FP, as well as on more challenging datasets like IJBB and IJBC.
