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This work investigates image synthesis of two generative models: the Variational Autoencoder (VAE) and the Denoising Diffusion Probabilistic Model (DDPM). One approach for synthesis is to interpolate between real samples within the models' latent spaces. To understand how we can interpolate in the models' latent space, we derive the loss functions of the models. We trained the models on MN
