# Fast Deep Predictive Coding Networks for Videos Feature Extraction without Labels

arXiv: https://arxiv.org/abs/2409.04945 · HTML: https://mlsanity.com/paper/2409.04945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 8 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach](https://mlsanity.com/paper/2105.04869/) (2021)
- [Constructing Neural Network-Based Models for Simulating Dynamical Systems](https://mlsanity.com/paper/2111.01495/) (2021)
- [Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition](https://mlsanity.com/paper/1805.07526/) (2018)
- [Deep Predictive Coding Network for Object Recognition](https://mlsanity.com/paper/1802.04762/) (2018)
- [Long-time predictive modeling of nonlinear dynamical systems using neural networks](https://mlsanity.com/paper/1805.12547/) (2018)
- [Deep Predictive Coding Networks](https://mlsanity.com/paper/1301.3541/) (2013)
- [Majorization-minimization procedures and convergence of SQP methods for semi-algebraic and tame programs](https://mlsanity.com/paper/1409.8147/) (2014)
- [Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks](https://mlsanity.com/paper/1611.00050/) (2016)
- [Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations](https://mlsanity.com/paper/2101.06848/) (2021)

## Abstract (arXiv)

Brain-inspired deep predictive coding networks (DPCNs) effectively model and capture video features through a bi-directional information flow, even without labels. They are based on an overcomplete description of video scenes, and one of the bottlenecks has been the lack of effective sparsification techniques to find discriminative and robust dictionaries. FISTA has been the best alternative. This paper proposes a DPCN with a fast inference of internal model variables (states and causes) that achieves high sparsity and accuracy of feature clustering. The proposed unsupervised learning procedure, inspired by adaptive dynamic programming with a majorization-minimization framework, and its convergence are rigorously analyzed. Experiments in the data sets CIFAR-10, Super Mario Bros video game, and Coil-100 validate the approach, which outperforms previous versions of DPCNs on learning rate, sparsity ratio, and feature clustering accuracy. Because of DCPN's solid foundation and explainability, this advance opens the door for general applications in object recognition in video without labels.