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Using back-propagation and its variants to train deep networks is oftenproblematic for new users. Issues such as exploding gradients, vanishinggradients, and high sensitivity to weight initialization strategies often makenetworks difficult to train, especially when users are experimenting with newarchitectures. Here, we present Local Representation Alignment (LRA), atraining procedure that is much less sensitive to bad initializations, does notrequire modifications to the network architecture, and can be adapted tonetworks with highly nonlinear and discrete-valued activation functions.Furthermore, we show that one variation of LRA can start with a nullinitialization of network weights and still successfully train networks with awide variety of nonlinearities, including tanh, ReLU-6, softplus, signum andothers that may draw their inspiration from biology.  A comprehensive set of experiments on MNIST and the much harder Fashion MNISTdata sets show that LRA can be used to train networks robustly and effectively,succeeding even when back-propagation fails and outperforming other alternativelearning algorithms, such as target propagation and feedback alignment.

Conducting Credit Assignment by Aligning Local Representations