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Iterative Adversarial Inference with ReInference Chain for Deep Graphical Models
Zhihao LIU Hui YIN Hua HUANG
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E102D
No.8
pp.15861589 Publication Date: 2019/08/01
Online ISSN: 17451361
DOI: 10.1587/transinf.2018EDL8256
Type of Manuscript: LETTER Category: Artificial Intelligence, Data Mining Keyword: deep graphical model, generative adversarial nets, latent variable, inference, generation,
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Summary:
Deep Graphical Model (DGM) based on Generative Adversarial Nets (GANs) has shown promise in image generation and latent variable inference. One of the typical models is the Iterative Adversarial Inference model (GibbsNet), which learns the joint distribution between the data and its latent variable. We present RGNet (Reinference GibbsNet) which introduces a reinference chain in GibbsNet to improve the quality of generated samples and inferred latent variables. RGNet consists of the generative, inference, and discriminative networks. An adversarial game is cast between the generative and inference networks and the discriminative network. The discriminative network is trained to distinguish between (i) the joint inferencelatent/dataspace pairs and reinferencelatent/dataspace pairs and (ii) the joint sampledlatent/generateddataspace pairs. We show empirically that RGNet surpasses GibbsNet in the quality of inferred latent variables and achieves comparable performance on image generation and inpainting tasks.

