# Temporal Autoencoder with U-Net Style Skip-Connections for Frame Prediction

arXiv: https://arxiv.org/abs/2011.12661 · HTML: https://mlsanity.com/paper/2011.12661/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

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

- [Dual Encoding U-Net for Spatio-Temporal Domain Shift Frame Prediction](https://mlsanity.com/paper/2110.11140/) (2021)
- [Attention Gate in Traffic Forecasting](https://mlsanity.com/paper/2109.13021/) (2021)

## Abstract (arXiv)

Finding sustainable and novel solutions to predict city-wide mobility behaviour is an ever-growing problem given increased urban complexity and growing populations. This paper seeks to address this by describing a traffic frame prediction approach that uses Convolutional LSTMs to create a Temporal Autoencoder with U-Net style skip-connections that marry together recurrent and traditional computer vision techniques to capture spatio-temporal dependencies at different scales without losing topological details of a given city. Utilisation of Cyclical Learning Rates is also presented, improving training efficiency by achieving lower loss scores in fewer epochs than standard approaches.