Neural Echos: Depthwise Convolutional Filters Replicate Biological
  Receptive Fields
Zahra Babaiee Peyman M. Kiasari Daniela Rus Radu Grosu
In this study, we present evidence suggesting that depthwise convolutional
kernels are effectively replicating the structural intricacies of the
biological receptive fields observed in the mammalian retina. We provide
analytics of trained kernels from various state-of-the-art models
substantiating this evidence. Inspired by this intriguing discovery, we propose
an initialization scheme that draws inspiration from the biological receptive
fields. Experimental analysis of the ImageNet dataset with multiple CNN
architectures featuring depthwise convolutions reveals a marked enhancement in
the accuracy of the learned model when initialized with biologically derived
weights. This underlies the potential for biologically inspired computational
models to further our understanding of vision processing systems and to improve
the efficacy of convolutional networks.