Understanding Deep Neural Networks via Linear Separability of Hidden
  Layers
Chao Zhang Xinyu Chen Wensheng Li Lixue Liu Wei Wu Dacheng Tao
In this paper, we measure the linear separability of hidden layer outputs to
study the characteristics of deep neural networks. In particular, we first
propose Minkowski difference based linear separability measures (MD-LSMs) to
evaluate the linear separability degree of two points sets. Then, we
demonstrate that there is a synchronicity between the linear separability
degree of hidden layer outputs and the network training performance, i.e., if
the updated weights can enhance the linear separability degree of hidden layer
outputs, the updated network will achieve a better training performance, and
vice versa. Moreover, we study the effect of activation function and network
size (including width and depth) on the linear separability of hidden layers.
Finally, we conduct the numerical experiments to validate our findings on some
popular deep networks including multilayer perceptron (MLP), convolutional
neural network (CNN), deep belief network (DBN), ResNet, VGGNet, AlexNet,
vision transformer (ViT) and GoogLeNet.