IEICE TRANSACTIONS on Information
Iterative Adversarial Inference with Re-Inference Chain for Deep Graphical Models
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 (Re-inference GibbsNet) which introduces a re-inference 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 inference-latent/data-space pairs and re-inference-latent/data-space pairs and (ii) the joint sampled-latent/generated-data-space 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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.8 pp.1586-1589
- Publication Date
- 2019/08/01
- Publicized
- 2019/05/07
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8256
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Zhihao LIU
Beijing Jiaotong University
Hui YIN
Beijing Jiaotong University
Hua HUANG
Beijing Jiaotong University
Keyword
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Zhihao LIU, Hui YIN, Hua HUANG, "Iterative Adversarial Inference with Re-Inference Chain for Deep Graphical Models" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 8, pp. 1586-1589, August 2019, doi: 10.1587/transinf.2018EDL8256.
Abstract: 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 (Re-inference GibbsNet) which introduces a re-inference 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 inference-latent/data-space pairs and re-inference-latent/data-space pairs and (ii) the joint sampled-latent/generated-data-space 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8256/_p
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@ARTICLE{e102-d_8_1586,
author={Zhihao LIU, Hui YIN, Hua HUANG, },
journal={IEICE TRANSACTIONS on Information},
title={Iterative Adversarial Inference with Re-Inference Chain for Deep Graphical Models},
year={2019},
volume={E102-D},
number={8},
pages={1586-1589},
abstract={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 (Re-inference GibbsNet) which introduces a re-inference 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 inference-latent/data-space pairs and re-inference-latent/data-space pairs and (ii) the joint sampled-latent/generated-data-space 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.},
keywords={},
doi={10.1587/transinf.2018EDL8256},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Iterative Adversarial Inference with Re-Inference Chain for Deep Graphical Models
T2 - IEICE TRANSACTIONS on Information
SP - 1586
EP - 1589
AU - Zhihao LIU
AU - Hui YIN
AU - Hua HUANG
PY - 2019
DO - 10.1587/transinf.2018EDL8256
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2019
AB - 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 (Re-inference GibbsNet) which introduces a re-inference 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 inference-latent/data-space pairs and re-inference-latent/data-space pairs and (ii) the joint sampled-latent/generated-data-space 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.
ER -
English
