# Unsupervised Learning Layers for Video Analysis

## TL;DR

This paper introduces two unsupervised learning layers for label-free video analysis, enabling neural networks to extract shape and motion features from videos in both unsupervised and semi-supervised settings.

## Contribution

The paper proposes novel UL layers for fully connected and convolutional layers, with closed-form and online learning algorithms, enhancing unsupervised video feature extraction.

## Key findings

- UL layers effectively extract shape and motion features from videos.
- Neural networks with UL layers perform well in head orientation estimation.
- UL layers demonstrate potential in moving object localization.

## Abstract

This paper presents two unsupervised learning layers (UL layers) for label-free video analysis: one for fully connected layers, and the other for convolutional ones. The proposed UL layers can play two roles: they can be the cost function layer for providing global training signal; meanwhile they can be added to any regular neural network layers for providing local training signals and combined with the training signals backpropagated from upper layers for extracting both slow and fast changing features at layers of different depths. Therefore, the UL layers can be used in either pure unsupervised or semi-supervised settings. Both a closed-form solution and an online learning algorithm for two UL layers are provided. Experiments with unlabeled synthetic and real-world videos demonstrated that the neural networks equipped with UL layers and trained with the proposed online learning algorithm can extract shape and motion information from video sequences of moving objects. The experiments demonstrated the potential applications of UL layers and online learning algorithm to head orientation estimation and moving object localization.