  This paper reveals a new appeal of the recently emerged large-kernel
Convolutional Neural Networks (ConvNets): as the teacher in Knowledge
Distillation (KD) for small-kernel ConvNets. While Transformers have led
state-of-the-art (SOTA) performance in various fields with ever-larger models
and labeled data, small-kernel ConvNets are considered more suitable for
resource-limited applications due to the efficient convolution operation and
compact weight sharing. KD is widely used to boost the performance of
small-kernel ConvNets. However, previous research shows that it is not quite
effective to distill knowledge (e.g., global information) from Transformers to
small-kernel ConvNets, presumably due to their disparate architectures. We
hereby carry out a first-of-its-kind study unveiling that modern large-kernel
ConvNets, a compelling competitor to Vision Transformers, are remarkably more
effective teachers for small-kernel ConvNets, due to more similar
architectures. Our findings are backed up by extensive experiments on both
logit-level and feature-level KD ``out of the box", with no dedicated
architectural nor training recipe modifications. Notably, we obtain the
\textbf{best-ever pure ConvNet} under 30M parameters with \textbf{83.1\%} top-1
accuracy on ImageNet, outperforming current SOTA methods including ConvNeXt V2
and Swin V2. We also find that beneficial characteristics of large-kernel
ConvNets, e.g., larger effective receptive fields, can be seamlessly
transferred to students through this large-to-small kernel distillation. Code
is available at: \url{https://github.com/VITA-Group/SLaK}.
