  Data labeling in supervised learning is considered an expensive and
infeasible tool in some conditions. The self-supervised learning method is
proposed to tackle the learning effectiveness with fewer labeled data, however,
there is a lack of confidence in the size of labeled data needed to achieve
adequate results. This study aims to draw a baseline on the proportion of the
labeled data that models can appreciate to yield competent accuracy when
compared to training with additional labels. The study implements the
kaggle.com' cats-vs-dogs dataset, Mnist and Fashion-Mnist to investigate the
self-supervised learning task by implementing random rotations augmentation on
the original datasets. To reveal the true effectiveness of the pretext process
in self-supervised learning, the original dataset is divided into smaller
batches, and learning is repeated on each batch with and without the pretext
pre-training. Results show that the pretext process in the self-supervised
learning improves the accuracy around 15% in the downstream classification task
when compared to the plain supervised learning.
