  We propose a composable framework for latent space image augmentation that
allows for easy combination of multiple augmentations. Image augmentation has
been shown to be an effective technique for improving the performance of a wide
variety of image classification and generation tasks. Our framework is based on
the Variational Autoencoder architecture and uses a novel approach for
augmentation via linear transformation within the latent space itself. We
explore losses and augmentation latent geometry to enforce the transformations
to be composable and involuntary, thus allowing the transformations to be
readily combined or inverted. Finally, we show these properties are better
performing with certain pairs of augmentations, but we can transfer the latent
space to other sets of augmentations to modify performance, effectively
constraining the VAE's bottleneck to preserve the variance of specific
augmentations and features of the image which we care about. We demonstrate the
effectiveness of our approach with initial results on the MNIST dataset against
both a standard VAE and a Conditional VAE. This latent augmentation method
allows for much greater control and geometric interpretability of the latent
space, making it a valuable tool for researchers and practitioners in the
field.
