  It is well known that direct training of deep neural networks will generally
lead to poor results. A major progress in recent years is the invention of
various pretraining methods to initialize network parameters and it was shown
that such methods lead to good prediction performance. However, the reason for
the success of pretraining has not been fully understood, although it was
argued that regularization and better optimization play certain roles. This
paper provides another explanation for the effectiveness of pretraining, where
we show pretraining leads to a sparseness of hidden unit activation in the
resulting neural networks. The main reason is that the pretraining models can
be interpreted as an adaptive sparse coding. Compared to deep neural network
with sigmoid function, our experimental results on MNIST and Birdsong further
support this sparseness observation.
