Generative Adversarial Networks (GANs) are part of the cutting edge in recent machine learning research. Trained by exploiting the interplay between a generative and a discriminative deep neural network, GANs are able to implicitly model the data distribution, thus allowing us to generate realistic data samples after model training is complete. The Digital Media sector… Continue reading AI4Media Workshop on GANs for Media Content Generation
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Generative Adversarial Networks in Multimedia Content Creation
Deep Convolutional Generative Adversarial Networks (DCGAN) have been used to generate highly compelling pictures or videos, such as manipulated facial animations, interior and outdoor images, videos. This lecture provides an extensive overview of several Generative Adversarial Networks applications for media production, notably for image content generation (e.g., human facial and body images), automatic image restyling/translation/captioning,… Continue reading Generative Adversarial Networks in Multimedia Content Creation