Towards Composable Distributions of Latent Space Augmentations

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Abstract

We propose a composable framework for latent space image augmentation, based on the Variational Autoencoder (VAE) architecture, that allows for easy combination of multiple augmentations through linear transformations within the latent space. This method explores losses and augmentation latent geometry to ensure transformations are composable and involuntary, enabling combinations or inversions, and effectively constrains the VAE’s bottleneck to preserve specific augmentation variances and image features. We demonstrate improved control and geometric interpretability of the latent space compared to standard and Conditional VAEs on the MNIST dataset.

Citation

Pooladzandi, Omead, Jeffrey Jiang, Sunay Bhat, and Gregory Pottie. 2023. “Towards Composable Distributions of Latent Space Augmentations.” arXiv preprint arXiv:2303.03462.

@article{pooladzandi2023composable,
  title={Towards Composable Distributions of Latent Space Augmentations},
  author={Pooladzandi, Omead and Jiang, Jeffrey and Bhat, Sunay and Pottie, Gregory},
  journal={arXiv preprint arXiv:2303.03462},
  year={2023}
}