Generative Methods

2020 ◽  
pp. 145-174
Keyword(s):  
Author(s):  
Yin Xu ◽  
Yan Li ◽  
Byeong-Seok Shin

Abstract With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.


Author(s):  
Raffi Kamalian ◽  
Alice M. Agogino ◽  
Hideyuki Takagi

In this paper we review the current state of automated MEMS synthesis with a focus on generative methods. We use the design of a MEMS resonator as a case study and explore the role that geometric constraints and human interaction play in a computer-aided MEMS design system based on genetic algorithms.


2011 ◽  
pp. 278-289
Author(s):  
Renato Saleri Lunazzi

The main goal of this chapter is to present a research project that consists of applying automatic generative methods in design processes. The initial approach briefly explores early theoretical conjectures, starting with form and function balance within former conceptual investigations. The following experiments describe original techniques introducing integrated 2-D and 3-D generators for the enhancement of recent 3-D Earth browsers (Virtual Terrain©, MSN Virtual Earth©, or Google Earth©) and cellularautomata processes for architectural programmatic optimization.


Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 80
Author(s):  
James Mountstephens ◽  
Jason Teo

Design is a challenging task that is crucial to all product development. Advances in design computing may allow machines to move from a supporting role to generators of design content. Generative Design systems produce designs by algorithms and offer the potential for the exploration of vast design spaces, the fostering of creativity, the combination of objective and subjective requirements, and the revolutionary integration of conceptual and detailed design phases. The application of generative methods to the design of discrete, physical, engineered products has not yet been reviewed. This paper reviews the Generative Product Design systems developed since 1998 in order to identify significant approaches and trends. Systems are analyzed according to their primary goal, generative method, the design phase they focus on, whether the generation is automatic or interactive, the number of design options they generate, and the types of design requirements involved in the generation process. Progress using this approach is recognized, and a number of challenges that must be addressed in order to achieve widespread acceptance are identified. Possible solutions are offered, including innovative approaches in Human–Computer Interaction.


Author(s):  
Thomas Blaschke ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Christian Margreitter ◽  
Christian Tyrchan ◽  
...  

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.


2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Christian Margreitter ◽  
Christian Tyrchan ◽  
...  

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.


2007 ◽  
Vol 60 (4-7) ◽  
pp. 341-350 ◽  
Author(s):  
David F. Dinges ◽  
Sundara Venkataraman ◽  
Eleanor L. McGlinchey ◽  
Dimitris N. Metaxas

2021 ◽  
Author(s):  
Haoran Shi ◽  
Zhibiao Rao ◽  
Yongning Wu ◽  
Zuohua Zhang ◽  
Chu Wang
Keyword(s):  

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