Statistical-mechanical analysis of pre-training and fine tuning in deep learning

研究成果: Article査読

6 被引用数 (Scopus)

抄録

In this paper, we present a statistical-mechanical analysis of deep learning. We elucidate some of the essential components of deep learning - pre-training by unsupervised learning and fine tuning by supervised learning. We formulate the extraction of features from the training data as a margin criterion in a high-dimensional feature-vector space. The self-organized classifier is then supplied with small amounts of labelled data, as in deep learning. Although we employ a simple single-layer perceptron model, rather than directly analyzing a multi-layer neural network, we find a nontrivial phase transition that is dependent on the number of unlabelled data in the generalization error of the resultant classifier. In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning. The analysis is performed by the replica method, which is a sophisticated tool in statistical mechanics. We validate our result in the manner of deep learning, using a simple iterative algorithm to learn the weight vector on the basis of belief propagation.

本文言語English
論文番号034003
ジャーナルjournal of the physical society of japan
84
3
DOI
出版ステータスPublished - 2015 3 15
外部発表はい

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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