Deep Belief Network Training Improvement Using Elite Samples Minimizing Free Energy

TL;DR

This paper introduces a novel training method for Deep Belief Networks that uses free energy to select elite samples, resulting in improved accuracy on benchmark datasets like MNIST and ISOLET.

Contribution

The paper proposes a new technique utilizing free energy to select elite samples during RBM training, enhancing the performance of Deep Belief Networks.

Findings

Achieved 0.99% error on MNIST test set.

Outperformed previous DBN and standard classifiers.

Reduced error rate to 3.59% on ISOLET dataset.

Abstract

Nowadays this is very popular to use deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. In this paper we present an improvement in a common method that is usually used in training of RBMs. The new method uses free energy as a criterion to obtain elite samples from generative model. We argue that these samples can more accurately compute gradient of log probability of training data. According to the results, an error rate of 0.99% was achieved on MNIST test set. This result shows that the proposed method outperforms the method presented in the first paper introducing DBN (1.25% error rate) and general classification methods such as SVM (1.4% error rate) and KNN (with 1.6% error rate). In another test using ISOLET dataset, letter classification error dropped to 3.59% compared to 5.59% error rate achieved in those papers using this dataset. The implemented method is available online at "http://ceit.aut.ac.ir/~keyvanrad/DeeBNet Toolbox.html".