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[3] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," in Proceedings of the International Conference on Learning Representations, 2015.
Deep learning-based image generation models typically consist of two components: a generator and a discriminator. The generator takes a random noise vector as input and produces a synthetic image, while the discriminator evaluates the generated image and tells the generator whether it is realistic or not. Through this process, the generator learns to produce images that are increasingly realistic, while the discriminator becomes more adept at distinguishing between real and fake images.
However, as the generator becomes more skilled at producing realistic images, it often becomes less capable of generating surprising images. This is because the generator tends to learn the modes of the training data distribution and produces images that are concentrated around these modes. As a result, generated images may lack diversity and surprise.
The concept of surprise is essential in image generation, as it enables the creation of images that are not only realistic but also unexpected. Surprise can be defined as the degree to which a generated image deviates from expectations, either in terms of its content, style, or both. Inducing surprise in generated images is crucial, as it can lead to more engaging, diverse, and interesting images.
The ability to generate realistic images has numerous applications in fields such as computer-aided design, video production, and virtual reality. Deep learning-based image generation models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have achieved remarkable success in generating highly realistic images. However, one of the key limitations of these models is their tendency to generate images that are often predictable and lack surprise.
[3] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," in Proceedings of the International Conference on Learning Representations, 2015.
Deep learning-based image generation models typically consist of two components: a generator and a discriminator. The generator takes a random noise vector as input and produces a synthetic image, while the discriminator evaluates the generated image and tells the generator whether it is realistic or not. Through this process, the generator learns to produce images that are increasingly realistic, while the discriminator becomes more adept at distinguishing between real and fake images.
However, as the generator becomes more skilled at producing realistic images, it often becomes less capable of generating surprising images. This is because the generator tends to learn the modes of the training data distribution and produces images that are concentrated around these modes. As a result, generated images may lack diversity and surprise.
The concept of surprise is essential in image generation, as it enables the creation of images that are not only realistic but also unexpected. Surprise can be defined as the degree to which a generated image deviates from expectations, either in terms of its content, style, or both. Inducing surprise in generated images is crucial, as it can lead to more engaging, diverse, and interesting images.
The ability to generate realistic images has numerous applications in fields such as computer-aided design, video production, and virtual reality. Deep learning-based image generation models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have achieved remarkable success in generating highly realistic images. However, one of the key limitations of these models is their tendency to generate images that are often predictable and lack surprise.
