Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning (1605.08104v5)

Abstract: While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network ("PredNet") architecture that is inspired by the concept of "predictive coding" from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. Altogether, these results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.

• The paper introduces PredNet, a novel neural network architecture that leverages predictive coding for unsupervised video prediction.
• It demonstrates superior performance on synthetic rotating faces and real-world driving videos by effectively learning latent representations.
• The model’s error-based recurrent design supports practical applications like steering angle estimation in autonomous driving.

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

The paper "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" by William Lotter, Gabriel Kreiman, and David Cox proposes a novel neural network architecture, termed "PredNet," which is inspired by the neuroscience concept of predictive coding. The authors explore the efficacy of unsupervised learning through the prediction of future frames in a video sequence. This work addresses critical challenges in leveraging unlabeled data to uncover the nuanced structure of the visual world.

Core Contributions

The primary contributions of the paper are:

1. PredNet Architecture: The PredNet model is built upon the idea of predictive coding, where layers in the network make local predictions and only forward deviations from these predictions to subsequent layers. This framework uniquely integrates both bottom-up and top-down connections in a recurrent convolutional structure.
2. Performance on Synthetic Data: Experiments on synthetic sequences of rotating faces indicate that the PredNet can reliably learn to predict future frames. The learned representations facilitate the decoding of underlying latent variables, such as object pose, enabling improved recognition capabilities with fewer training examples.
3. Scaling to Natural Image Streams: The applicability of PredNet is demonstrated on complex real-world data from car-mounted camera videos. The network effectively handles the dynamics of egocentric movement and the motion of objects in the visual scene. The learned representations are shown to support practical tasks such as estimating the car's steering angle.

Technical Approach

The PredNet is organized into stacked modules, where each layer (R_l) of the network generates a prediction (\hat{A}_l) for its input (A_l). This prediction is subtracted from the actual input to produce an error representation (E_l), which is then rectified into positive and negative populations and propagated through the network. The recurrent refinement of these predictions aligns with biological theories of brain function, particularly predictive coding.

The architecture's training process relies on backpropagation, optimizing the activity of error units (E_l). The authors use convolutional LSTM units within the representation neurons (R_l), allowing the model to maintain temporal continuity and capture complex temporal dependencies in video sequences.

Experimental Results

Synthetic Sequences

For the rotating faces dataset, the PredNet demonstrated robust predictive accuracy, outperforming baseline models including a traditional CNN-LSTM encoder-decoder. The learned representations effectively encoded latent parameters such as angular velocity and principal components of face identity. Notably, the model trained with error propagated across all layers (L_{all}) showed marginally reduced pixel-wise prediction accuracy but yielded richer representations beneficial for tasks like orientation-invariant face classification.

Natural Image Sequences

On the KITTI and CalTech Pedestrian datasets, the PredNet models performed well in predicting next-frame video sequences. They excelled in scenarios with complex motion, such as vehicles turning or shadows approaching. The error passing mechanism proved crucial, with the PredNet outperforming simplified models that omitted this feature. Additionally, the model's learned representation facilitated accurate linear decoding of steering angles from a car-mounted camera dataset, underscoring its practical utility in autonomous driving scenarios.

Implications and Future Directions

The results suggest that predictive coding mechanisms offer a powerful approach for unsupervised learning. The idea that accurate future frame predictions necessitate an implicit understanding of object structure and potential transformations has profound implications for both theoretical research and practical applications in AI.

Future research could explore the nature of representations learned by such architectures and explore extensions that incorporate probabilistic or adversarial elements into the error prediction mechanism. Enhancing models for better handling of long-term dependencies and integrating them with other modalities could further augment their capabilities in diverse domains.

Acknowledgments: The authors express gratitude to collaborators and acknowledge support from various funding sources including IARPA, NSF, and the Center for Brains, Minds, and Machines.

In conclusion, the PredNet architecture presents a significant step forward in utilizing predictive coding for unsupervised learning, demonstrating both theoretical depth and practical effectiveness in diverse video prediction tasks.