GADE: A Generative Adversarial Approach to Density Estimation and its Applications

Density estimation is a challenging unsupervised learning problem. Current maximum likelihood approaches for density estimation are either restrictive or incapable of producing high-quality samples. On the other hand, likelihood-free models such as generative adversarial networks, produce sharp samp...

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Bibliographic Details
Published in:International journal of computer vision 2020-11, Vol.128 (10-11), p.2731-2743
Main Authors: Abbasnejad, M. Ehsan, Shi, Javen, van den Hengel, Anton, Liu, Lingqiao
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Imaging
Computer Science
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Special Issue on Generative Adversarial Networks for Computer Vision
Specific gravity
Technology application
Vision
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Summary:Density estimation is a challenging unsupervised learning problem. Current maximum likelihood approaches for density estimation are either restrictive or incapable of producing high-quality samples. On the other hand, likelihood-free models such as generative adversarial networks, produce sharp samples without a density model. The lack of a density estimate limits the applications to which the sampled data can be put, however. We propose a generative adversarial density estimator (GADE), a density estimation approach that bridges the gap between the two. Allowing for a prior on the parameters of the model, we extend our density estimator to a Bayesian model where we can leverage the predictive variance to measure our confidence in the likelihood. Our experiments on challenging applications such as visual dialog or autonomous driving where the density and the confidence in predictions are crucial shows the effectiveness of our approach.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-020-01360-9