This report details an investigation into the applicability of Vector-Quantised Variational Autoencoders (VQ-VAEs) for representation learning. The VQ-VAE’s representations are investigated and a novel regularisation technique involving noisy vector quantisation is introduced. It is then shown, through a range of quantitative and qualitative experiments, that this regularisation technique forces the VQ-VAE to learn superior representations. A theoretical explanation into why this is the case is also provided. In addition to this a new prior is fitted to the VQ-VAE latent space in the form of an adapted Progressive Growing Generative Adversarial Network (PGAN) with self attention which yields a significant reduction in training and sampling time.