  Object recognition in the presence of background clutter and distractors is a
central problem both in neuroscience and in machine learning. However, the
performance level of the models that are inspired by cortical mechanisms,
including deep networks such as convolutional neural networks and deep belief
networks, is shown to significantly decrease in the presence of noise and
background objects [19, 24]. Here we develop a computational framework that is
hierarchical, relies heavily on key properties of the visual cortex including
mid-level feature selectivity in visual area V4 and Inferotemporal cortex (IT)
[4, 9, 12, 18], high degrees of selectivity and invariance in IT [13, 17, 18]
and the prior knowledge that is built into cortical circuits (such as the
emergence of edge detector neurons in primary visual cortex before the onset of
the visual experience) [1, 21], and addresses the problem of object recognition
in the presence of background noise and distractors. Our approach is
specifically designed to address large deformations, allows flexible
communication between different layers of representation and learns highly
selective filters from a small number of training examples.
