Fixing the problems of deep neural networks will require better training
  data and learning algorithms
Drew Linsley Thomas Serre
Bowers and colleagues argue that DNNs are poor models of biological vision
because they often learn to rival human accuracy by relying on strategies that
differ markedly from those of humans. We show that this problem is worsening as
DNNs are becoming larger-scale and increasingly more accurate, and prescribe
methods for building DNNs that can reliably model biological vision.