  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 generators with just a few lines
of code. We show that training with the Hessian Penalty often causes
axis-aligned disentanglement to emerge in latent space when applied to ProGAN
on several datasets. Additionally, we use our regularization term to identify
interpretable directions in BigGAN's latent space in an unsupervised fashion.
Finally, we provide empirical evidence that the Hessian Penalty encourages
substantial shrinkage when applied to over-parameterized latent spaces.
