(b) We propose the Vector-Pixel Fusion Variational Autoencoder (VP-VAE, Sec. 3.2) within a transformer-based architecture to encode vector embeddings alongside pixel-level features …

Dec 11, 2024 · The core idea of SVGFusion is to utilize a popular Text-to-Image framework to learn a continuous latent space for vector graphics. Specifically, SVGFusion comprises two …

A variational autoencoder is a generative model with a prior and noise distribution respectively. Usually such models are trained using the expectation-maximization meta-algorithm (e.g. …

Feb 22, 2020 · The Variational Autoencoder. Now that we have both the encoder and the decode network fully defined, it’s time to wrap them together into one autoencoder model. This can …

“Visual similarity between SVGs is learned by a class-conditioned, convolutional variational autoencoder (VAE) on a rendered representation (blue). The class label and learned …

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 …

Dec 22, 2021 · A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide a quick and simple working …

Variational Autoencoders (VAEs) address some of the limitations of traditional autoencoders by introducing a probabilistic approach to encoding and decoding. The motivation behind VAEs …

Jun 6, 2019 · Abstract: Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an …

Variational autoencoder architecture. The earliest type of generative machine learning model. Inspired by https://towardsdatascience.com/intuitively-understanding-variational-autoencoders …