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Human-Machine Co-Ideation Via Combinational Generative Model

https://doi.org/10.2139/ssrn.4678929
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Data mining and machine learning in computational creativity
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A Style-Based Generator Architecture for Generative Adversarial Networks
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Disentangled Image Generation Through Structured Noise Injection
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Editing in Style: Uncovering the Local Semantics of GANs
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Analyzing and Improving the Image Quality of StyleGAN
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00832openalex-extracted
Image2StyleGAN++: How to Edit the Embedded Images?
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01411openalex-extracted
Adversarial Latent Autoencoders
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Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
inferredcites →https://doi.org/10.1109/iccv.2017.244openalex-extracted
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
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Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
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On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance Based on Analogical Distance, Commonness, and Modality of Examples
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Comparing Ideation Techniques for Beginning Designers
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A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval
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Mining for Creativity: Determining the Creativity of Ideas Through Data Mining Techniques
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A comparison of digital and conventional sketching: implications for conceptual design ideation
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A review of design intelligence: progress, problems, and challenges
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The Creative Mind
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
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Spatially Controllable Image Synthesis with Internal Representation Collaging
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Swapping Autoencoder for Deep Image Manipulation
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A Comparison of Consensus, Consistency, and Measurement Approaches to Estimating Interrater Reliability
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