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Affine Variational Autoencoders: An Efficient Approach for Improving Generalization and Robustness to Distribution Shift
[article]
2019
arXiv
pre-print
In this study, we propose the Affine Variational Autoencoder (AVAE), a variant of Variational Autoencoder (VAE) designed to improve robustness by overcoming the inability of VAEs to generalize to distributional shifts in the form of affine perturbations. By optimizing an affine transform to maximize ELBO, the proposed AVAE transforms an input to the training distribution without the need to increase model complexity to model the full distribution of affine transforms. In addition, we introduce
arXiv:1905.05300v1
fatcat:cfogazghcjfatcrbyapbfbx4we