Spotlight
Residual Flows for Invertible Generative Modeling
Tian Qi Chen · Jens Behrmann · David Duvenaud · Joern-Henrik Jacobsen

Tue Dec 10th 04:40 -- 04:45 PM @ West Exhibition Hall C + B3

Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood. Invertible residual networks provide a flexible family of transformations where only Lipschitz conditions rather than strict architectural constraints are needed for enforcing invertibility. However, prior work trained invertible residual networks for density estimation by relying on biased log-density estimates whose bias increased with the network's expressiveness. We give a tractable unbiased estimate of the log density, and reduce the memory required during training by a factor of ten. Furthermore, we improve invertible residual blocks by proposing the use of activation functions that avoid gradient saturation and generalizing the Lipschitz condition to induced mixed norms. The resulting approach, called Residual Flows, achieves state-of-the-art performance on density estimation amongst flow-based models, and outperforms networks that use coupling blocks at joint generative and discriminative modeling.

Author Information

Ricky Tian Qi Chen (U of Toronto)
Jens Behrmann (University of Bremen)
David Duvenaud (University of Toronto)
Joern-Henrik Jacobsen (Vector Institute)

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