May 14, 2020 · The autoencoder is trained to minimize the difference between the input $x$ and the reconstruction $\hat{x}$ using a kind of reconstruction loss. Because the autoencoder is …

Apr 26, 2023 · Generating images with a specific label. In order to generate an image with a specific label, our AE needs to learn how to decode the latent variable when given a hint.

variational autoencoder (VAE). The parameters of both the encoder and decoder networks are updated using a single pass of ordinary backprop. The reconstruction term corresponds to …

Jan 8, 2024 · This article is about conditional variational autoencoders (CVAE) and requires a minimal understanding of this type of model. If you are not familiar with CVAEs, I can …

Sep 12, 2024 · Adding labels to both the input and output of a Conditional Variational Autoencoder (CVAE) enables controlled generation, improved disentanglement, and better …

Explore the power of Conditional Variational Autoencoders (CVAEs) through this implementation trained on the MNIST dataset to generate handwritten digit images based on class labels. …

Dec 21, 2016 · A variational autoencoder generating images according to given labels. The grid of images below was produced by fixing the desired number input to the decoder and taking a …

The variational autoencoder (VAE) has succeeded in learning disentangled latent representations from data without supervision. Well disentangled representations can express interpretable …

els because it is challenging to ac-quire categorical information as latent variables. There-fore, we propose a framework for learning label . epresen-tations in a VAE by using supervised …

learn a direct map from inputs x to the class labels Generative classifiers learn a model of joint probability p(x;y) and make their predictions by using the Bayes rule to calculate