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Training a deep convolutional neural net typically starts with a randominitialisation of all filters in all layers which severely reduces the forwardsignal and back-propagated error and leads to slow and sub-optimal training.Techniques that counter that focus on either increasing the signal orincreasing the gradients adaptively but the model behaves very differently atthe beginning of training compared to later when stable pathways through thenet have been established. To compound this problem the effective minibatchsize varies greatly between layers at different depths and between individualfilters as activation sparsity typically increases with depth leading to areduction in effective learning rate since gradients may superpose rather thanadd and this further compounds the covariate shift problem as deeper neuronsare less able to adapt to upstream shift.  Proposed here is a method of automatic gain control of the signal built intoeach convolutional neuron that achieves equivalent or superior performance thanbatch normalisation and is compatible with single sample or minibatch gradientdescent. The same model is used both for training and inference.  The technique comprises a scaled per sample map mean subtraction from the rawconvolutional filter output followed by scaling of the difference.

Deep Control - a simple automatic gain control for memory efficient and high performance training of deep convolutional neural networks