Deep Face Detector Adaptation Without Negative Transfer or Catastrophic Forgetting

Muhammad Abdullah Jamal, Haoxiang Li, Boqing Gong; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5608-5618

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.