Learning Likelihoods with Conditional Normalizing Flows

29 Nov 2019Christina WinklerDaniel WorrallEmiel HoogeboomMax Welling

Normalizing Flows (NFs) are able to model complicated distributions p(y) with strong inter-dimensional correlations and high multimodality by transforming a simple base density p(z) through an invertible neural network under the change of variables formula. Such behavior is desirable in multivariate structured prediction tasks, where handcrafted per-pixel loss-based methods inadequately capture strong correlations between output dimensions... (read more)

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