# Super-resolution Variational Auto-Encoders

arXiv: https://arxiv.org/abs/2006.05218 · HTML: https://mlsanity.com/paper/2006.05218/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality](https://mlsanity.com/paper/2211.08944/) (2022)
- [Image Super-Resolution With Deep Variational Autoencoders](https://mlsanity.com/paper/2203.09445/) (2022)
- [Time-Causal VAE: Robust Financial Time Series Generator](https://mlsanity.com/paper/2411.02947/) (2024)
- [A Modular Conditional Diffusion Framework for Image Reconstruction](https://mlsanity.com/paper/2411.05993/) (2024)
- [Super-resolution Reconstruction of Single Image for Latent features](https://mlsanity.com/paper/2211.12845/) (2022)
- [Diverse super-resolution with pretrained deep hiererarchical VAEs](https://mlsanity.com/paper/2205.10347/) (2022)
- [Quantum Down Sampling Filter for Variational Auto-encoder](https://mlsanity.com/paper/2501.06259/) (2025)

## Abstract (arXiv)

The framework of variational autoencoders (VAEs) provides a principled method for jointly learning latent-variable models and corresponding inference models. However, the main drawback of this approach is the blurriness of the generated images. Some studies link this effect to the objective function, namely, the (negative) log-likelihood. Here, we propose to enhance VAEs by adding a random variable that is a downscaled version of the original image and still use the log-likelihood function as the learning objective. Further, by providing the downscaled image as an input to the decoder, it can be used in a manner similar to the super-resolution. We present empirically that the proposed approach performs comparably to VAEs in terms of the negative log-likelihood, but it obtains a better FID score in data synthesis.