  Deep learning has achieved outstanding performance for face recognition
benchmarks, but performance reduces significantly for low resolution (LR)
images. We propose an attention similarity knowledge distillation approach,
which transfers attention maps obtained from a high resolution (HR) network as
a teacher into an LR network as a student to boost LR recognition performance.
Inspired by humans being able to approximate an object's region from an LR
image based on prior knowledge obtained from HR images, we designed the
knowledge distillation loss using the cosine similarity to make the student
network's attention resemble the teacher network's attention. Experiments on
various LR face related benchmarks confirmed the proposed method generally
improved recognition performances on LR settings, outperforming
state-of-the-art results by simply transferring well-constructed attention
maps. The code and pretrained models are publicly available in the
https://github.com/gist-ailab/teaching-where-to-look.
