  Recent improvements in Generative Adversarial Neural Networks (GANs) have
shown their ability to generate higher quality samples as well as to learn good
representations for transfer learning. Most of the representation learning
methods based on GANs learn representations ignoring their post-use scenario,
which can lead to increased generalisation ability. However, the model can
become redundant if it is intended for a specific task. For example, assume we
have a vast unlabelled audio dataset, and we want to learn a representation
from this dataset so that it can be used to improve the emotion recognition
performance of a small labelled audio dataset. During the representation
learning training, if the model does not know the post emotion recognition
task, it can completely ignore emotion-related characteristics in the learnt
representation. This is a fundamental challenge for any unsupervised
representation learning model. In this paper, we aim to address this challenge
by proposing a novel GAN framework: Guided Generative Neural Network (GGAN),
which guides a GAN to focus on learning desired representations and generating
superior quality samples for audio data leveraging fewer labelled samples.
Experimental results show that using a very small amount of labelled data as
guidance, a GGAN learns significantly better representations.
