Variational autoencoder (VAE) [3] is a generative model widely used in image reconstruction and generation tasks. It provides a more efficient way (e.g. in comparison to a standard …

Apr 18, 2020 · In this post, we want to introduce the variational autoencoder (VAE) and use it to generate new images of handwritten digits by using MNIST as training data. VAE is a …

May 30, 2018 · We observe a low loss value for the vanilla autoencoder with 30D bottleneck size which results in high-quality reconstructed images. Although loss values increased in VAEs, …

Oct 1, 2018 · A VAE maps an image to two vectors, z_mean and z_log_sigma, which define a probability distribution over the latent space, used to sample a latent point to decode. In …

Dec 9, 2024 · In this article, we will walk through building a Variational Autoencoder (VAE) in PyTorch for image reconstruction. VAEs are a class of generative models designed for …

both a standard (entangled) VAE and a vanilla supervised model. Results show that the disentangled VAE significantly outperforms the other two models when the proportion of …

Nov 10, 2020 · In this tutorial, we will be discussing how to train a variational autoencoder (VAE) with Keras (TensorFlow, Python) from scratch. We will be concluding our study with the …

Code release for Super-Resolution Variational Auto-Encoders. The framework of Variational Auto-Encoders (VAEs) provides a principled manner of reasoning in latent-variable models using …

Mar 4, 2023 · Regular autoencoders get an image as input and output the same image. However, Variational AutoEncoders (VAE) generate new images with the same distribution as the …

Have you ever wondered what the differences between a variational autoencoder (VAE) and a generative adversarial network (GAN) are? Well, in this article I’m going to take a look at both …