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The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement
[article]
2020
arXiv
pre-print
Existing disentanglement methods for deep generative models rely on hand-picked priors and complex encoder-based architectures. In this paper, we propose the Hessian Penalty, a simple regularization term that encourages the Hessian of a generative model with respect to its input to be diagonal. We introduce a model-agnostic, unbiased stochastic approximation of this term based on Hutchinson's estimator to compute it efficiently during training. Our method can be applied to a wide range of deep
arXiv:2008.10599v1
fatcat:odaoj42xjzggrpstpzn4xr2n4a