  State-of-the-art results in deep learning have been improving steadily, in
good part due to the use of larger models. However, widespread use is
constrained by device hardware limitations, resulting in a substantial
performance gap between state-of-the-art models and those that can be
effectively deployed on small devices. While Knowledge Distillation (KD)
theoretically enables small student models to emulate larger teacher models, in
practice selecting a good student architecture requires considerable human
expertise. Neural Architecture Search (NAS) appears as a natural solution to
this problem but most approaches can be inefficient, as most of the computation
is spent comparing architectures sampled from the same distribution, with
negligible differences in performance. In this paper, we propose to instead
search for a family of student architectures sharing the property of being good
at learning from a given teacher. Our approach AutoKD, powered by Bayesian
Optimization, explores a flexible graph-based search space, enabling us to
automatically learn the optimal student architecture distribution and KD
parameters, while being 20x more sample efficient compared to existing
state-of-the-art. We evaluate our method on 3 datasets; on large images
specifically, we reach the teacher performance while using 3x less memory and
10x less parameters. Finally, while AutoKD uses the traditional KD loss, it
outperforms more advanced KD variants using hand-designed students.
