2010 WhyDoesUnsupervisedPreTrainingH

Subject Headings: Deep Learning, Pre-Training, Unsupervised Pre-Training.

Notes

Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtained in several areas, mostly on vision and language data sets. The best results obtained on supervised learning tasks involve an unsupervised learning component, usually in an unsupervised pre-training phase. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. The main question investigated here is the following: how does unsupervised pre-training work? Answering this questions is important if learning in deep architectures is to be further improved. We propose several explanatory hypotheses and test them through extensive simulations. We empirically show the influence of pre-training with respect to architecture depth, model capacity, and database record count training examples. The experiments confirm and clarify the advantage of unsupervised pre-training. The results suggest that unsupervised pre-training guides the learning towards basins of attraction of minima that support better generalization from the training data set; the evidence from these results supports a regularization explanation for the effect of pre-training.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 WhyDoesUnsupervisedPreTrainingH	Yoshua Bengio Dumitru Erhan Aaron Courville Pierre-Antoine Manzagol Pascal Vincent Samy Bengio			Why Does Unsupervised Pre-training Help Deep Learning?						2010