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Recent improvements in Generative Adversarial Neural Networks (GANs) haveshown their ability to generate higher quality samples as well as to learn goodrepresentations for transfer learning. Most of the representation learningmethods based on GANs learn representations ignoring their post-use scenario,which can lead to increased generalisation ability. However, the model canbecome redundant if it is intended for a specific task. For example, assume wehave a vast unlabelled audio dataset, and we want to learn a representationfrom this dataset so that it can be used to improve the emotion recognitionperformance of a small labelled audio dataset. During the representationlearning training, if the model does not know the post emotion recognitiontask, it can completely ignore emotion-related characteristics in the learntrepresentation. This is a fundamental challenge for any unsupervisedrepresentation learning model. In this paper, we aim to address this challengeby proposing a novel GAN framework: Guided Generative Neural Network (GGAN),which guides a GAN to focus on learning desired representations and generatingsuperior quality samples for audio data leveraging fewer labelled samples.Experimental results show that using a very small amount of labelled data asguidance, a GGAN learns significantly better representations.

Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data