Which type of Variational Autoencoder (VAE) variant focuses on generating structured and diverse  samples by conditioning the generation process on additional information?Question 1Answera.InfoVAEb.Beta-VAEc.Adversarial Autoencoder (AAE)d.Conditional VAE (CVAE

In a Variational Autoencoder (VAE), which component is responsible for encoding input data into a latent space representation?Question 27Answera. Encoderb.Latent Spacec.Decoderd.Sampler

What is a common architectural choice for the encoder network in Variational Autoencoders (VAEs) to capture complex dependencies in the input data?Question 2Answera.Convolutional layersb.Recurrent layersc.Recurrent layersd.Fully connected layers

So far, I’ve written about three types of generative models, GAN, VAE, and Flow-based models. They have shown great success in generating high-quality samples, but each has some limitations of its own. GAN models are known for potentially unstable training and less diversity in generation due to their adversarial training nature. VAE relies on a surrogate loss. Flow models have to use specialized architectures to construct reversible transform.

In the formulation of Generative Adversarial Networks (GANs), which term represents the objective of the generator to match the distribution of generated samples to that of real data?Question 8Answera.Generator lossb.Adversarial lossc. Reconstruction lossd.Discriminator loss

Variational AutoEncoder (use reconstruction error as the outlier score)