# Unsupervised Feature Learning from Temporal Data

arXiv: https://arxiv.org/abs/1504.02518 · HTML: https://mlsanity.com/paper/1504.02518/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2015.

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

- [Embed and Emulate: Contrastive representations for simulation-based inference](https://mlsanity.com/paper/2409.18402/) (2024)
- [Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning](https://mlsanity.com/paper/2109.06737/) (2021)

## Abstract (arXiv)

Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.