  Curriculum learning is a bio-inspired training technique that is widely
adopted to machine learning for improved optimization and better training of
neural networks regarding the convergence rate or obtained accuracy. The main
concept in curriculum learning is to start the training with simpler tasks and
gradually increase the level of difficulty. Therefore, a natural question is
how to determine or generate these simpler tasks. In this work, we take
inspiration from Spatial Transformer Networks (STNs) in order to form an
easy-to-hard curriculum. As STNs have been proven to be capable of removing the
clutter from the input images and obtaining higher accuracy in image
classification tasks, we hypothesize that images processed by STNs can be seen
as easier tasks and utilized in the interest of curriculum learning. To this
end, we study multiple strategies developed for shaping the training
curriculum, using the data generated by STNs. We perform various experiments on
cluttered MNIST and Fashion-MNIST datasets, where on the former, we obtain an
improvement of $3.8$pp in classification accuracy compared to the baseline.
