  Neural Architecture Search (NAS) provides state-of-the-art results when
trained on well-curated datasets with annotated labels. However, annotating
data or even having balanced number of samples can be a luxury for
practitioners from different scientific fields, e.g., in the medical domain. To
that end, we propose a NAS-based framework that bears the threefold
contributions: (a) we focus on the self-supervised scenario, i.e., where no
labels are required to determine the architecture, and (b) we assume the
datasets are imbalanced, (c) we design each component to be able to run on a
resource constrained setup, i.e., on a single GPU (e.g. Google Colab). Our
components build on top of recent developments in self-supervised
learning~\citep{zbontar2021barlow}, self-supervised NAS~\citep{kaplan2020self}
and extend them for the case of imbalanced datasets. We conduct experiments on
an (artificially) imbalanced version of CIFAR-10 and we demonstrate our
proposed method outperforms standard neural networks, while using $27\times$
less parameters. To validate our assumption on a naturally imbalanced dataset,
we also conduct experiments on ChestMNIST and COVID-19 X-ray. The results
demonstrate how the proposed method can be used in imbalanced datasets, while
it can be fully run on a single GPU. Code is available
\href{https://github.com/TimofeevAlex/ssnas_imbalanced}{here}.
