Tractable Density Estimation on Learned Manifolds with Conformal Embedding FlowsDownload PDF

21 May 2021, 20:46 (modified: 09 Nov 2021, 19:37)NeurIPS 2021 PosterReaders: Everyone
Keywords: generative modelling, normalizing flows, injective flows, image generation, manifold learning, conformal
TL;DR: We introduce Conformal Embedding Flows which can model data confined to low-dimensional manifolds while preserving tractable likelihoods.
Abstract: Normalizing flows are generative models that provide tractable density estimation via an invertible transformation from a simple base distribution to a complex target distribution. However, this technique cannot directly model data supported on an unknown low-dimensional manifold, a common occurrence in real-world domains such as image data. Recent attempts to remedy this limitation have introduced geometric complications that defeat a central benefit of normalizing flows: exact density estimation. We recover this benefit with Conformal Embedding Flows, a framework for designing flows that learn manifolds with tractable densities. We argue that composing a standard flow with a trainable conformal embedding is the most natural way to model manifold-supported data. To this end, we present a series of conformal building blocks and apply them in experiments with synthetic and real-world data to demonstrate that flows can model manifold-supported distributions without sacrificing tractable likelihoods.
Supplementary Material: pdf
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
Code: https://github.com/layer6ai-labs/CEF
17 Replies

Loading