Which framework is characterized by the simultaneous training of a generator and a discriminator?

The framework characterized by the simultaneous training of a generator and a discriminator is the Generative Adversarial Network (GAN). In a GAN, the generator's role is to create synthetic data that is intended to resemble real data, while the discriminator's task is to evaluate the authenticity of the data, distinguishing between real and generated samples. This adversarial process pushes both components to improve over time, as the generator attempts to produce more realistic data to fool the discriminator, while the discriminator works to become more adept at identifying what is real versus what is generated.

This dual training process is what makes GANs particularly powerful and interesting in the realm of generative models, allowing them to create high-quality images, audio, and other types of data. Other frameworks listed do not involve this kind of competitive training between two networks. For instance, Convolutional Neural Networks (CNNs) are primarily used for tasks like image classification and do not involve a generator-discriminator setup. Similarly, Recurrent Neural Networks (RNNs) focus on sequential data processing, and Support Vector Machines (SVMs) are geared towards classification tasks rather than generative capabilities. Thus, GANs uniquely exemplify the simultaneous training mechanism described in the question.