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Natural Image Manipulation for Autoregressive Models Using Fisher Scores
[article]
2020
arXiv
pre-print
Deep autoregressive models are one of the most powerful models that exist today which achieve state-of-the-art bits per dim. However, they lie at a strict disadvantage when it comes to controlled sample generation compared to latent variable models. Latent variable models such as VAEs and normalizing flows allow meaningful semantic manipulations in latent space, which autoregressive models do not have. In this paper, we propose using Fisher scores as a method to extract embeddings from an
arXiv:1912.05015v2
fatcat:h5odp5iyp5fbhe3y22fjif4xyy