Abstract
We explore the application of neural networks in generating realistic and diverse faces for the advertisement industry. The study involves a thorough literature review of generative models up to StyleGAN2, and investigating its potential impact on the creation of compelling visuals for advertising campaigns. We develop a new framework for generating images for targeted advertising with automatic face detection. We propose a method for collecting images for creating massive training datasets. We check some variants of generative models to arrive at using CNNs with additional style modules and the Frechet inception distance.
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Acknowledgments
This paper concerns the promotion of the results of the project No POIR.01.01.01-00-0912/18-00, “Development of an automated system using artificial intelligence for multi-channel recruitment advertising and Real-Time-Bidding operations using recurrent neural networks and Generative Adversarial Networks”, co-financed by the European Union.
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Osowski, M., Krasnodebska, A., Drozda, P., Scherer, R. (2024). Professionally Diverse: AI-Generated Faces for Targeted Advertising. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2024. Lecture Notes in Computer Science(), vol 14795. Springer, Singapore. https://doi.org/10.1007/978-981-97-4982-9_14
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DOI: https://doi.org/10.1007/978-981-97-4982-9_14
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