scholarly journals Differentially Private Generative Adversarial Networks with Model Inversion

2021 ◽  
Author(s):  
Dongjie Chen ◽  
Sen-ching Samson Cheung ◽  
Chen-Nee Chuah ◽  
Sally Ozonoff
Keyword(s):  
Generative Adversarial Networks ◽  
Model Inversion ◽  
Adversarial Networks

scholarly journals You Get What You Sow: High Fidelity Image Synthesis with a Single Pretrained Network

2021 ◽  
Author(s):  
Kefeng Zhu ◽  
Peilin Tong ◽  
Hongwei Kan ◽  
Rengang Li
Keyword(s):  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Synthesis Methods ◽  
Specific Information ◽  
High Fidelity ◽  
Model Inversion ◽  
Adversarial Networks ◽  
Training Requirement ◽  
Knowledge Distillation ◽  
Novel Strategy

State-of-the-art image synthesis methods are mostly based on generative adversarial networks and require large dataset and extensive training. Although the model-inversion-oriented branch of methods eliminate the training requirement, the quality of the resulting image tends to be limited due to the lack of sufficient natural and class-specific information. In this paper, we introduce a novel strategy for high fidelity image synthesis with a single pretrained classification network. The strategy includes a class-conditional natural regularization design and a corresponding metadata collecting procedure for different scenarios. We show that our method can synthesize high quality natural images that closely follow the features of one or more given seed images. Moreover, our method achieves surprisingly decent results in the task of sketch-based image synthesis without training. Finally, our method further improves the performance in terms of accuracy and efficiency in the data-free knowledge distillation task.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

A Primer on Generative Adversarial Networks

2020 ◽  
Author(s):  
Dr. Vikas Thada ◽  
Mr. Utpal Shrivastava ◽  
Jyotsna Sharma ◽  
Kuwar Prateek Singh ◽  
Manda Ranadeep
Keyword(s):  
Generative Adversarial Networks ◽  
Adversarial Networks
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