Overview and implementation of a Deep Belief Network (DBN) for image classification, reconstruction and adversial attack performance

Introduction

The biological inspiration of Articial Neural Networks as well as a brief theoretical background of DBNs is given. A DBN can be seen as the stacking of several Restricted Boltzmann Machines, having the advantage of RBMs which learn a representation of the data through unsupervised learning. A linear classifier can be coupled to the last layer to have a linear readout for e.g. classification.

Implementation

A DBN is trained using the FashionMNIST dataset

Visual processing

Learned weights through the layers are visualized to observe what the model has learned.

Linear read outs

A linear model is added to have a linear read out for classification

Comparison with a Feed Forward Neural Network

We compare our DBN with a FFN for benchmarking.

Internal representation of the model

We use observe how the model clustered together similarities in the data at each hidden layer.

Robustness to noise

Observe the impact that noise has on the DBN accuracy.

Adversial attacks

We explore how the DBN classifies when faced against adversarial attacks, when using both reconstruction of the original image and when not.

Few shot learning

Finally the DBN was fine-tuned with a linear classifier that is trained on only a small number of samples, achieving a high accuracy due to the internal representation being already learned while training.