Which generative model is specifically designed for producing images?

The correct choice for a generative model specifically designed for producing images is Generative Adversarial Networks (GANs). GANs consist of two neural networks, a generator and a discriminator, that work against each other to create realistic images from random noise. The generator's goal is to produce data that closely resembles the real dataset, while the discriminator's goal is to differentiate between real and generated images. This adversarial process continues until the generator produces highly convincing images that the discriminator can no longer distinguish from real images.

In comparison, Variational Autoencoders (VAEs) are also capable of generating images but they are typically used for different purposes such as data compression and reconstruction alongside generation. Recurrent Neural Networks (RNNs) are primarily used for sequence prediction and handling sequential data rather than generating images. Convolutional Neural Networks (CNNs) are designed for image recognition and classification tasks, not specifically for generative purposes.

Given these distinctions, GANs stand out as the model explicitly designed for image generation due to their unique architecture and generative capability.