1 research outputs found
Multi-pretrained Deep Neural Network
Pretraining is widely used in deep neutral network and one of the most famous
pretraining models is Deep Belief Network (DBN). The optimization formulas are
different during the pretraining process for different pretraining models. In
this paper, we pretrained deep neutral network by different pretraining models
and hence investigated the difference between DBN and Stacked Denoising
Autoencoder (SDA) when used as pretraining model. The experimental results show
that DBN get a better initial model. However the model converges to a
relatively worse model after the finetuning process. Yet after pretrained by
SDA for the second time the model converges to a better model if finetuned