  Using back-propagation and its variants to train deep networks is often
problematic for new users. Issues such as exploding gradients, vanishing
gradients, and high sensitivity to weight initialization strategies often make
networks difficult to train, especially when users are experimenting with new
architectures. Here, we present Local Representation Alignment (LRA), a
training procedure that is much less sensitive to bad initializations, does not
require modifications to the network architecture, and can be adapted to
networks with highly nonlinear and discrete-valued activation functions.
Furthermore, we show that one variation of LRA can start with a null
initialization of network weights and still successfully train networks with a
wide variety of nonlinearities, including tanh, ReLU-6, softplus, signum and
others that may draw their inspiration from biology.
  A comprehensive set of experiments on MNIST and the much harder Fashion MNIST
data sets show that LRA can be used to train networks robustly and effectively,
succeeding even when back-propagation fails and outperforming other alternative
learning algorithms, such as target propagation and feedback alignment.
