The power of pictures: using ML assisted image generation to engage the\n crowd in complex socioscientific problems

2020/10/15 by Janet Rafner, Lotte Philipsen, Rafner, Janet +8
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Image Processing Techniques #Artificial Intelligence (cs.AI) #Cell Image Analysis Techniques #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Human-Computer Interaction (cs.HC)

paper · pdf · doi:10.48550/arxiv.2010.12324

openalex publication_date 2020/10/15 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28

Abstract

Human-computer image generation using Generative Adversarial Networks (GANs)\nis becoming a well-established methodology for casual entertainment and open\nartistic exploration. Here, we take the interaction a step further by weaving\nin carefully structured design elements to transform the activity of\nML-assisted imaged generation into a catalyst for large-scale popular dialogue\non complex socioscientific problems such as the United Nations Sustainable\nDevelopment Goals (SDGs) and as a gateway for public participation in research.\n

Citations

• Improved Techniques for Training GANs
• Outline Colorization through Tandem Adversarial Networks
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Neural Painters: A learned differentiable constraint for generating brushstroke paintings
• Transform the Set: Memory Attentive Generation of Guided and Unguided\n Image Collages
• Machine learning based co-creative design framework
• crea.blender: A Neural Network-Based Image Generation Game to Assess\n Creativity

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