  In the backdrop of increasing data requirements of Deep Neural Networks for
object recognition that is growing more untenable by the day, we present
Developmental PreTraining (DPT) as a possible solution. DPT is designed as a
curriculum-based pre-training approach designed to rival traditional
pre-training techniques that are data-hungry. These training approaches also
introduce unnecessary features that could be misleading when the network is
employed in a downstream classification task where the data is sufficiently
different from the pre-training data and is scarce. We design the curriculum
for DPT by drawing inspiration from human infant visual development. DPT
employs a phased approach where carefully-selected primitive and universal
features like edges and shapes are taught to the network participating in our
pre-training regime. A model that underwent the DPT regime is tested against
models with randomised weights to evaluate the viability of DPT.
