scholarly journals Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 52
Author(s):  
Marios Zachariou ◽  
Neofytos Dimitriou ◽  
Ognjen Arandjelović
Keyword(s):  
Empirical Evaluation ◽  
Semantic Content ◽  
Generative Adversarial Networks ◽  
Conceptual Problem ◽  
Artistic Style ◽  
Adversarial Networks ◽  
Large Numbers ◽  
Processing Step ◽  
Visual Reconstruction ◽  
Ancient Coin

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

scholarly journals Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

Sci ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 13 ◽  
Author(s):  
Marios Zachariou ◽  
Neofytos Dimitriou ◽  
Ognjen Arandjelović
Keyword(s):  
Empirical Evaluation ◽  
Semantic Content ◽  
Generative Adversarial Networks ◽  
Conceptual Problem ◽  
Artistic Style ◽  
Adversarial Networks ◽  
Large Numbers ◽  
Processing Step ◽  
Visual Reconstruction ◽  
Ancient Coin

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

scholarly journals Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons

2020 ◽  
Vol 34 (07) ◽  
pp. 10909-10916
Author(s):  
Ligong Han ◽  
Ruijiang Gao ◽  
Mun Kim ◽  
Xin Tao ◽  
Bo Liu ◽  
...  
Keyword(s):  
Generative Adversarial Networks ◽  
Pairwise Comparisons ◽  
Generative Adversarial Network ◽  
Weak Supervision ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Large Numbers ◽  
Supervised Methods ◽  
Adversarial Training ◽  
Noise Tolerant

Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. In the light of Bayesian uncertainty estimation and noise-tolerant adversarial training, PC-GAN can estimate attribute rating efficiently and demonstrate robust performance in noise resistance. Through extensive experiments, we show both qualitatively and quantitatively that PC-GAN performs comparably with fully-supervised methods and outperforms unsupervised baselines. Code and Supplementary can be found on the project website*.

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.

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