  Distillation-aware Neural Architecture Search (DaNAS) aims to search for an
optimal student architecture that obtains the best performance and/or
efficiency when distilling the knowledge from a given teacher model. Previous
DaNAS methods have mostly tackled the search for the neural architecture for
fixed datasets and the teacher, which are not generalized well on a new task
consisting of an unseen dataset and an unseen teacher, thus need to perform a
costly search for any new combination of the datasets and the teachers. For
standard NAS tasks without KD, meta-learning-based computationally efficient
NAS methods have been proposed, which learn the generalized search process over
multiple tasks (datasets) and transfer the knowledge obtained over those tasks
to a new task. However, since they assume learning from scratch without KD from
a teacher, they might not be ideal for DaNAS scenarios. To eliminate the
excessive computational cost of DaNAS methods and the sub-optimality of rapid
NAS methods, we propose a distillation-aware meta accuracy prediction model,
DaSS (Distillation-aware Student Search), which can predict a given
architecture's final performances on a dataset when performing KD with a given
teacher, without having actually to train it on the target task. The
experimental results demonstrate that our proposed meta-prediction model
successfully generalizes to multiple unseen datasets for DaNAS tasks, largely
outperforming existing meta-NAS methods and rapid NAS baselines. Code is
available at https://github.com/CownowAn/DaSS
