Mar 13, 2020 · The autoencoder aims to map the input image to a multivariate normal distribution in the latent space. Variational autoencoder transforms input image into a remarkable output …

A novel variational autoencoder is developed to model images, as well as associated labels or captions. The Deep Generative Deconvolutional Network (DGDN) is used as a decoder of the …

Mar 17, 2022 · In this paper, we introduce VDVAE-SR, a new model that aims to exploit the most recent deep VAE methodologies to improve upon the results of similar models. VDVAE-SR …

This paper proposes a frequency quantized variational autoencoder (FQ-VAE) to address these issues. The proposed method transforms image features into linear combinations in the …

results show that the image research algorithm using variational autoencoder for image 1, image 2, and image 3 reduces the time by 3332, 2637, and 1470 bit respectively compared with the …

This paper analyzes the basic principles, architecture, and application of VAE in image generation, explores the advantages of VAE in improving image generation effects, and …

May 2, 2025 · Regularization of inverse problems is of paramount importance in computational imaging. The ability of neural networks to learn efficient image representations has been …

May 30, 2023 · In this paper, we first review the development and research status of traditional variational autoencoders and their variants, and summarize and compare the performance of …

In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate …

Variational autoencoders (VAE) are neural networks used for the unsuper-vised learning of complicated distributions by using stochastic variational infer-ence. Traditionally, they have …