  Deep Learning's outstanding track record across several domains has stemmed
from the use of error backpropagation (BP). Several studies, however, have
shown that it is impossible to execute BP in a real brain. Also, BP still
serves as an important and unsolved bottleneck for memory usage and speed. We
propose a simple, novel algorithm, the Front-Contribution algorithm, as a
compact alternative to BP. The contributions of all weights with respect to the
final layer weights are calculated before training commences and all the
contributions are appended to weights of the final layer, i.e., the effective
final layer weights are a non-linear function of themselves. Our algorithm then
essentially collapses the network, precluding the necessity for weight updation
of all weights not in the final layer. This reduction in parameters results in
lower memory usage and higher training speed. We show that our algorithm
produces the exact same output as BP, in contrast to several recently proposed
algorithms approximating BP. Our preliminary experiments demonstrate the
efficacy of the proposed algorithm. Our work provides a foundation to
effectively utilize these presently under-explored "front contributions", and
serves to inspire the next generation of training algorithms.
