  Recent advances in deep learning techniques have achieved remarkable
performance in several computer vision problems. A notably intuitive technique
called Curriculum Learning (CL) has been introduced recently for training deep
learning models. Surprisingly, curriculum learning achieves significantly
improved results in some tasks but marginal or no improvement in others. Hence,
there is still a debate about its adoption as a standard method to train
supervised learning models. In this work, we investigate the impact of
curriculum learning in crowd counting using the density estimation method. We
performed detailed investigations by conducting 112 experiments using six
different CL settings using eight different crowd models. Our experiments show
that curriculum learning improves the model learning performance and shortens
the convergence time.
