Unsupervised Representation Learning using Wasserstein Generative Adversarial Network

Abstract

In recent years, representational learning has attracted considerable attention. However, unsupervised representation learning has received less attention compared to supervised representation learning. This paper introduces a combination of a deep neural network (DNN) and a generative adversarial network (GAN) that can learn features through unsupervised learning. Essentially, the Generative Adversarial Network (GAN) is a deep learning architecture that engages two neural networks in a framework similar to a zero-sum game. Generating new, synthetic data that resembles a known data distribution is the aim of GANs. In June 2014, Ian Goodfellow and associates first developed the idea of Generative Adversarial Network (GAN). The research used a new type of GAN model which is called Wasserstein GAN. There are some distinct differences between traditional GAN and Wasserstein GAN. This paper highlights the differences and benefits of using Wasserstein GAN, as well as the architecture of Wasserstein GAN. This study trained the model on an image dataset to extract features, and subsequently tested it on another dataset, demonstrating that the GAN model learns a hierarchy of representation from object parts in the discriminator. The purpose of this paper is to use unsupervised learning like Convolutional Neural Network (CNN) and Wasserstein Generative Adversarial Network (WGAN) for feature extraction from unlabeled dataset.