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TECHNIQUES FOR ANALYZING VEHICLE DESIGN DEVIATIONS USING DEEP LEARNING WITH NEURAL NETWORKS
TECHNIQUES FOR ANALYZING VEHICLE DESIGN DEVIATIONS USING DEEP LEARNING WITH NEURAL NETWORKS
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机译:基于神经网络的深度学习的车辆设计偏差分析技术
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摘要
A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.
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