%0 Conference Proceedings
%T Creative Intelligence – Automating Car Design Studio with Generative Adversarial Networks (GAN)
%+ PES University [Bengaluru]
%A Radhakrishnan, Sreedhar
%A Bharadwaj, Varun
%A Manjunath, Varun
%A Srinath, Ramamoorthy
%Z Part 1: MAKE-Main Track
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Hamburg, Germany
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-11015
%P 160-175
%8 2018-08-27
%D 2018
%R 10.1007/978-3-319-99740-7_11
%K Computational creativity
%K Generative Adversarial Networks
%K Automobile design
%K Deep learning
%K Computer vision
%K Sketching filter
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X In this paper, we propose and implement a system based on Generative Adversarial Networks (GANs), to create novel car designs from a minimal design studio sketch. A key component of our architecture is a novel convolutional filter layer, that produces sketches similar to those drawn by designers during rapid prototyping. The sketches produced are more aesthetic than the ones from standard edge detection filters or gradient operations. In addition, we show that our system is able to generate hitherto unseen perspectives of a car, given a sketch of the car at just a single viewing angle. For extensive training, testing and validation of our system, we have developed a comprehensive, paired dataset of around 100,000 car images (with transparent backgrounds) and their respective sketches. Our work augments human intelligence and creativity using machine learning and deep neural networks. Our system has the significant benefit of reducing the cycle time in the sketch-to-image process which has largely been considered a creative domain. This is achieved by learning to interpret a preliminary sketch drawn by a designer, to generate novel visual designs in a matter of seconds, which may otherwise require considerable time and effort. While the system enhances the productivity of the designer, the machine learning enhanced design visualizations can cut costs during the product prototyping stage. Our system exhibits good impactful potential for the automobile industry and can be easily adapted to industries which require creative intelligence.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-02060050/document
%2 https://inria.hal.science/hal-02060050/file/472936_1_En_11_Chapter.pdf
%L hal-02060050
%U https://inria.hal.science/hal-02060050
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-11015