  Recent theoretical and empirical work in statistical machine learning has
demonstrated the importance of learning algorithms for deep architectures,
i.e., function classes obtained by composing multiple non-linear
transformations. Self-taught learning (exploiting unlabeled examples or
examples from other distributions) has already been applied to deep learners,
but mostly to show the advantage of unlabeled examples. Here we explore the
advantage brought by {\em out-of-distribution examples}. For this purpose we
developed a powerful generator of stochastic variations and noise processes for
character images, including not only affine transformations but also slant,
local elastic deformations, changes in thickness, background images, grey level
changes, contrast, occlusion, and various types of noise. The
out-of-distribution examples are obtained from these highly distorted images or
by including examples of object classes different from those in the target test
set. We show that {\em deep learners benefit more from out-of-distribution
examples than a corresponding shallow learner}, at least in the area of
handwritten character recognition. In fact, we show that they beat previously
published results and reach human-level performance on both handwritten digit
classification and 62-class handwritten character recognition.
