  Knowledge distillation (KD) has been applied to various tasks successfully,
and mainstream methods typically boost the student model via spatial imitation
losses. However, the consecutive downsamplings induced in the spatial domain of
teacher model is a type of corruption, hindering the student from analyzing
what specific information needs to be imitated, which results in accuracy
degradation. To better understand the underlying pattern of corrupted feature
maps, we shift our attention to the frequency domain. During frequency
distillation, we encounter a new challenge: the low-frequency bands convey
general but minimal context, while the high are more informative but also
introduce noise. Not each pixel within the frequency bands contributes equally
to the performance. To address the above problem: (1) We propose the Frequency
Prompt plugged into the teacher model, absorbing the semantic frequency context
during finetuning. (2) During the distillation period, a pixel-wise frequency
mask is generated via Frequency Prompt, to localize those pixel of interests
(PoIs) in various frequency bands. Additionally, we employ a position-aware
relational frequency loss for dense prediction tasks, delivering a high-order
spatial enhancement to the student model. We dub our Frequency Knowledge
Distillation method as FreeKD, which determines the optimal localization and
extent for the frequency distillation. Extensive experiments demonstrate that
FreeKD not only outperforms spatial-based distillation methods consistently on
dense prediction tasks (e.g., FreeKD brings 3.8 AP gains for RepPoints-R50 on
COCO2017 and 4.55 mIoU gains for PSPNet-R18 on Cityscapes), but also conveys
more robustness to the student. Notably, we also validate the generalization of
our approach on large-scale vision models (e.g., DINO and SAM).
