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.

以满足日益增长的深度神经网络对目标识别的数据需求为背景，本文提出了一种可能的解决方案，即发展性预训练（DPT），它是一种基于课程的预训练方法，旨在与那些对数据需求很大的传统预训练技术竞争。本文从人类婴儿视觉发展中汲取灵感，设计了 DPT 的课程，并采用阶段性的方法向网络传授精心选择的基本和普遍特征。通过将经历了 DPT 训练的模型与具有随机权重的模型进行对比评估，验证了 DPT 的可行性。