Developmental Pretraining (DPT) for Image Classification Networks
Niranjan Rajesh Debayan Gupta
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.