Able to generate new data samples by sampling from a learned distribution in the latent space rather than from a latent vector
Models the latent space as a probability distribution
The latent space serves as a continuous, structured representation of the input data
Loss function = reconstruction loss + latent loss (KL divergence)
Small latent loss tends to result in generated images closely resembling those present in the training set but lacking in visual quality
Small reconstruction loss leads to well-reconstructed images during training but hampers the generation of novel images that deviate significantly from the training set
