  Prevailing image-translation frameworks mostly seek to process images via the
end-to-end style, which has achieved convincing results. Nonetheless, these
methods lack interpretability and are not scalable on different
image-translation tasks (e.g., style transfer, HDR, etc.). In this paper, we
propose an interpretable knowledge-based image-translation framework, which
realizes the image-translation through knowledge retrieval and transfer. In
details, the framework constructs a plug-and-play and model-agnostic general
purpose knowledge library, remembering task-specific styles, tones, texture
patterns, etc. Furthermore, we present a fast ANN searching approach, Bandpass
Hierarchical K-Means (BHKM), to cope with the difficulty of searching in the
enormous knowledge library. Extensive experiments well demonstrate the
effectiveness and feasibility of our framework in different image-translation
tasks. In particular, backtracking experiments verify the interpretability of
our method. Our code soon will be available at
https://github.com/AceSix/Knowledge_Transfer.
