# Predicting Visual Memory Schemas with Variational Autoencoders

arXiv: https://arxiv.org/abs/1907.08514 · HTML: https://mlsanity.com/paper/1907.08514/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Memorability: An image-computable measure of information utility](https://mlsanity.com/paper/2104.00805/) (2021)
- [Modeling Visual Memorability Assessment with Autoencoders Reveals Characteristics of Memorable Images](https://mlsanity.com/paper/2410.15235/) (2024)

## Abstract (arXiv)

Visual memory schema (VMS) maps show which regions of an image cause that image to be remembered or falsely remembered. Previous work has succeeded in generating low resolution VMS maps using convolutional neural networks. We instead approach this problem as an image-to-image translation task making use of a variational autoencoder. This approach allows us to generate higher resolution dual channel images that represent visual memory schemas, allowing us to evaluate predicted true memorability and false memorability separately. We also evaluate the relationship between VMS maps, predicted VMS maps, ground truth memorability scores, and predicted memorability scores.