  Recent advances in deep learning have led to significant progress in the
computer vision field, especially for visual object recognition tasks. The
features useful for object classification are learned by feed-forward deep
convolutional neural networks (CNNs) automatically, and they are shown to be
able to predict and decode neural representations in the ventral visual pathway
of humans and monkeys. However, despite the huge amount of work on optimizing
CNNs, there has not been much research focused on linking CNNs with guiding
principles from the human visual cortex. In this work, we propose a network
optimization strategy inspired by both of the developmental trajectory of
children's visual object recognition capabilities, and Bar (2003), who
hypothesized that basic level information is carried in the fast magnocellular
pathway through the prefrontal cortex (PFC) and then projected back to inferior
temporal cortex (IT), where subordinate level categorization is achieved. We
instantiate this idea by training a deep CNN to perform basic level object
categorization first, and then train it on subordinate level categorization. We
apply this idea to training AlexNet (Krizhevsky et al., 2012) on the ILSVRC
2012 dataset and show that the top-5 accuracy increases from 80.13% to 82.14%,
demonstrating the effectiveness of the method. We also show that subsequent
transfer learning on smaller datasets gives superior results.
