Abstract
Psychologists define latent learning as learning that occurs without task-specific reinforcement and is not demonstrated until needed. Since this knowledge is acquired while mastering some other task(s), it is a form of transfer learning. We utilize latent learning to enable a deep neural net to distinguish among a set of handwritten numerals. The accuracies obtained are compared to those achievable with a simplistic ‘group-mean’ classification technique, which is explained later in this paper. The deep neural net architecture used was a Le-Net 5 [3] convolutional neural net with only minor differences in the output layer.
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