  Machine learning has had a great deal of success in image processing.
However, the focus of this work has largely been on realistic images, ignoring
more niche art styles such as pixel art. Additionally, many traditional machine
learning models that focus on groups of pixels do not work well with pixel art,
where individual pixels are important. We propose the Pixel VQ-VAE, a
specialized VQ-VAE model that learns representations of pixel art. We show that
it outperforms other models in both the quality of embeddings as well as
performance on downstream tasks.
