scholarly journals Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis

2021 ◽  
Author(s):  
Amin Heyrani Nobari ◽  
Wei Chen ◽  
Faez Ahmed
Keyword(s):  
Generative Model ◽  
Inverse Design ◽  
Data Driven ◽  
Generative Adversarial Network ◽  
Design Problems ◽  
Design Synthesis ◽  
Adversarial Network ◽  
Average Improvement ◽  
Inverse Design Problem ◽  
Alternative Designs

Abstract Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among those approaches, the conditional deep generative model shows great potential since 1) it works for complex high-dimensional designs and 2) it can generate multiple alternative designs given any condition. In this work, we propose a conditional deep generative model, Range-GAN, to achieve automatic design synthesis subject to range constraints. The proposed model addresses the sparse conditioning issue in data-driven inverse design problems by introducing a label-aware self-augmentation approach. We also propose a new uniformity loss to ensure generated designs evenly cover the given requirement range. Through a real-world example of constrained 3D shape generation, we show that the label-aware self-augmentation leads to an average improvement of 14% on the constraint satisfaction for generated 3D shapes, and the uniformity loss leads to a 125% average increase on the uniformity of generated shapes’ attributes. This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space. For further information and code for this paper please refer to http://decode.mit.edu/projects/rangegan/.

RANGE-GAN: Design Synthesis Under Constraints Using Conditional Generative Adversarial Networks

2021 ◽  
pp. 1-16
Author(s):  
Amin Heyrani Nobari ◽  
Wei (Wayne) Chen ◽  
Faez Ahmed
Keyword(s):  
Generative Model ◽  
Inverse Design ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Design Problems ◽  
Design Synthesis ◽  
Average Improvement ◽  
Multiple Alternative ◽  
Inverse Design Problem ◽  
Alternative Designs

Abstract Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among those approaches, the conditional deep generative model shows great potential since 1) it works for complex high-dimensional designs and 2) it can generate multiple alternative designs given any condition. In this work, we propose a conditional deep generative model, Range-GAN, to achieve automatic design synthesis subject to range constraints. The proposed model addresses the sparse conditioning issue in data-driven inverse design problems by introducing a label-aware self-augmentation approach. We also propose a new uniformity loss to ensure generated designs evenly cover the given requirement range. Through a real-world example of constrained 3D shape generation, we show that the label-aware self-augmentation leads to an average improvement of14% on the constraint satisfaction for generated 3D shapes, and the uniformity loss leads to a 125% average increase on the uniformity of generated shapes' attributes. This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space.

PaDGAN: Learning to Generate High-Quality Novel Designs

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Wei Chen ◽  
Faez Ahmed
Keyword(s):  
Loss Function ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Generative Models ◽  
Training Data ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Design Synthesis ◽  
Adversarial Network

Abstract Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: (1) generated designs lack diversity and do not cover all areas of the design space, (2) it is difficult to explicitly improve the overall performance or quality of generated designs, and (3) existing models generally do not generate novel designs, outside the domain of the training data. In this article, we simultaneously address these challenges by proposing a new determinantal point process-based loss function for probabilistic modeling of diversity and quality. With this new loss function, we develop a variant of the generative adversarial network, named “performance augmented diverse generative adversarial network” (PaDGAN), which can generate novel high-quality designs with good coverage of the design space. By using three synthetic examples and one real-world airfoil design example, we demonstrate that PaDGAN can generate diverse and high-quality designs. In comparison to a vanilla generative adversarial network, on average, it generates samples with a 28% higher mean quality score with larger diversity and without the mode collapse issue. Unlike typical generative models that usually generate new designs by interpolating within the boundary of training data, we show that PaDGAN expands the design space boundary outside the training data towards high-quality regions. The proposed method is broadly applicable to many tasks including design space exploration, design optimization, and creative solution recommendation.

scholarly journals Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 319
Author(s):  
Wang Xi ◽  
Guillaume Devineau ◽  
Fabien Moutarde ◽  
Jie Yang
Keyword(s):  
Data Augmentation ◽  
Human Movement ◽  
Generative Models ◽  
Generative Model ◽  
Great Success ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Human Movements ◽  
Action Type ◽  
Skeleton Image

Generative models for images, audio, text, and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The objective of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using tree structure skeleton image format. We evaluate our approach on the 3D skeletal data provided in the large NTU_RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of the 60 action classes. We also quantitatively evaluate the performance of our model by computing Fréchet inception distances, which shows strong correlation to human judgement. To the best of our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.

scholarly journals Generative Enhancement of 3D Image Classifiers

2020 ◽  
Author(s):  
Michal Varga ◽  
Ján Jadlovský ◽  
Slávka Jadlovská
Keyword(s):  
Knowledge Sharing ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Classification Performance ◽  
Generative Model ◽  
3D Image ◽  
Accuracy Improvement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling

Abstract In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the strengths of both non-generative classifiers and generative modeling. Its purpose is to streamline the creation of new classifiers by embedding existing compatible classifiers in a generative network architecture. The demonstration of this process and evaluation of its effects is performed using a 3D convolutional classifier and its generative equivalent - a conditional generative adversarial network classifier. The results show that the generative model achieves greater classification performance, gaining a relative classification accuracy improvement of 7.43%. Improvement of accuracy is also present when compared to a plain convolutional classifier trained on a dataset augmented with examples produced by a trained generator. This suggests there is a desirable knowledge sharing within the hybrid discriminator-classifier network.

scholarly journals Generative Model for Skeletal Human Movements based on conditional DC-GAN applied to pseudo-images

2020 ◽  
Author(s):  
Wang Xi ◽  
Guillaume Devineau ◽  
Fabien Moutarde ◽  
Jie Yang
Keyword(s):  
Data Augmentation ◽  
Human Movement ◽  
Generative Models ◽  
Generative Model ◽  
Great Success ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Human Movements ◽  
Action Type ◽  
Skeleton Image

Generative models for images, audio, text and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The object of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using Tree Structure Skeleton Image format. We evaluate our approach on the 3D-skeleton data provided in the large NTU RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of its 60 action classes. We also quantitatively evaluate the performance of our model by computing Frechet Inception Distances, which shows strong correlation to human judgement. Up to our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Inverse 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.

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Inverse 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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