Abstract
Despite deep neural networks have
demonstrated strong power in face photo-sketch synthesis task, their
performance, however, are still limited by the lack of training data
(photo-sketch pairs). Knowledge Transfer (KT), which aims at training a smaller
and fast student network with the information learned from a larger and
accurate teacher network, has attracted much attention recently due to its
superior performance in the acceleration and compression of deep neural
networks. This work has brought us great inspiration that we can train a
relatively small student network on very few training data by transferring
knowledge from a larger teacher model trained on enough training data for other
tasks. Therefore, we propose a novel knowledge transfer framework to synthesize
face photos from face sketches or synthesize face sketches from face photos.
Particularly, we utilize two teacher networks trained on large amount of data
in related task to learn the knowledge of face photos and face sketches
separately and transfer them to two student networks simultaneously. In
addition, the two student networks, one for photo ? sketch task and the other for sketch ? photo task, can transfer their knowledge mutually. With the
proposed method, we can train our model which has superior performance using a
small set of photo-sketch pairs. We validate the effectiveness of our method
across several datasets. Quantitative and qualitative evaluations illustrate
that our model outperforms other state-of-the-art methods in generating face
sketches (or photos) with high visual quality and recognition ability.
Keywords
- Computer Vision: Biometrics, Face and Gesture Recognition
- Computer Vision: Computer Vision
