Deep Face Detector Adaptation
without Negative Transfer or Catastrophic Forgetting
Abstract
Arguably, no single face detector fits all real-life scenarios. It is often desirable to have some built-in schemes for
a face detector to automatically adapt, e.g., to a particular user’s photo album (the target domain). We propose a
novel face detector adaptation approach that works as long
as there are representative images of the target domain no
matter they are labeled or not and, more importantly, without the need of accessing the training data of the source
domain. Our approach explicitly accounts for the notorious negative transfer caveat in domain adaptation thanks
to a residual loss by design. Moreover, it does not incur
catastrophic interference with the knowledge learned from
the source domain and, therefore, the adapted face detectors maintain about the same performance as the old detectors in the original source domain. As such, our adaption
approach to face detectors is analogous to the popular interpolation techniques for language models; it may opens a
new direction for progressively training the face detectors
domain by domain. We report extensive experimental results to verify our approach on two massively benchmarked
face detectors