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Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection
[article]
2020
arXiv
pre-print
Neural random fields (NRFs), referring to a class of generative models that use neural networks to implement potential functions in random fields (a.k.a. energy-based models), are not new but receive less attention with slow progress. Different from various directed graphical models such as generative adversarial networks (GANs), NRFs provide an interesting family of undirected graphical models for generative modeling. In this paper we propose a new approach, the inclusive-NRF approach, to
arXiv:1806.00271v5
fatcat:kvo3vg3ayjfcjjpmntveptk6pm