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Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
William Lotter, Gabriel Kreiman, David Cox
TL;DR
Unsupervised visual learning remains difficult because supervised vision systems depend on large labeled datasets. The paper introduces PredNet, which predicts future video frames through predictive-coding-inspired local predictions and error propagation. Across synthetic and natural videos, the learned representations support latent-parameter decoding, recognition, and steering-angle estimation, motivating prediction as an unsupervised learning framework.
Problem
Learning useful visual representations from unlabeled data, especially with few views rather than large labeled image datasets, remains an unsolved problem.
Method
PredNet is a deep recurrent convolutional architecture whose layers make local future-frame predictions and forward deviations from those predictions.
Results
PredNet learns representations that decode latent object parameters and support recognition on synthetic sequences, while natural-video representations support steering-angle decoding and prediction of camera and object movement.
Takeaways & Limitations
The results support future-frame prediction as an unsupervised learning signal for implicitly learning object and scene structure.
Abstract
from arXiv · showhide
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.
1 INTRODUCTION
The paper addresses the unsolved problem of learning useful visual representations without large labeled datasets. It proposes predicting future video frames so models can learn object and scene structure from temporal experience.
- Large labeled image datasets limit deep learning’s utility in domains where such data are unavailable.
- Visual experience includes both viewer self-motion and movement of objects, unlike static-image training.
- Prediction of future frames is proposed as an unsupervised signal because it requires an implicit model of object structure and transformations.
- PredNet uses a deep recurrent convolutional network with bottom-up and top-down connections to continually predict future video frames.
- Synthetic and natural-video experiments show representations useful for decoding latent object parameters, predicting camera and object movement, and estimating steering angle.
2 THE PREDNET MODEL
PredNet stacks recurrent modules that predict their inputs and forward only prediction errors upward. Its training objective minimizes weighted error activity across time and network layers.
- Each module predicts its input on the next frame, subtracts that prediction from the actual input, and forwards the resulting error to the next layer.
- The recurrent representation layer receives both its own error signal and top-down input from the next network level.
- For video, the lowest-layer target is the image sequence itself, while higher-layer targets derive from transformed errors from the layer below.
- Representation neurons use convolutional LSTM units, and predictions are generated from recurrent representations through convolution and rectification.
- Prediction errors are split into rectified positive and negative populations before concatenation and propagation.
- The loss minimizes a time- and layer-weighted sum of error-unit activity, equivalent to an L1 error at each layer.
3 EXPERIMENTS
Experiments show that PredNet learns to predict rotating synthetic faces and complex car-camera videos, while its representations support latent-variable decoding, transformation-tolerant recognition, and steering-angle estimation.
- 3.1 RENDERED IMAGE SEQUENCES: Synthetic rotating-face experiments evaluate next-frame prediction and representation learning using rendered faces with random identities, orientations, and constant pan and roll velocities.The dataset uses 16K training sequences and 800 validation and test sequences.
- 3.1 RENDERED IMAGE SEQUENCES: PredNet accumulates temporal information to predict future frames, progressing from uniform and blurry initial outputs to predictions that closely match incoming frames.The model adapts to sequence dynamics after observing motion.
- 3.1 RENDERED IMAGE SEQUENCES: Trained PredNets enable better linear decoding of latent face factors than randomly initialized models, especially pan rotation speed, while all-layer loss improves first-principal-component decoding over lowest-layer loss.Decoded factors include pan and roll velocities, initial pan angle, and the first face-space principal component.
- 3.1 RENDERED IMAGE SEQUENCES: PredNet representations compare favorably with a standard autoencoder and Ladder Network variant for orientation-invariant face classification across training-set sizes, with Lall outperforming L0.The result suggests predictive training can support recognition under object transformations.
- 3.2 NATURAL IMAGE SEQUENCES: On natural car-camera videos, PredNet makes fairly accurate predictions of vehicle motion, self-motion effects, and newly revealed scene regions, including difficult turning scenarios.The model was trained on KITTI and evaluated on CalTech Pedestrian sequences; temporally scrambled input provides an additional control.
- 3.2 NATURAL IMAGE SEQUENCES: PredNet outperforms Brabandere et al. by 29% on a similar car-camera dataset, outperforms Mathieu et al. on Human3.6M, and underperforms Finn et al. without hyperparameter optimization.These comparisons assess the model against published video-prediction systems.
4 DISCUSSION
The paper presents predictive coding as an unsupervised learning signal: predicting future frames supports internal representations of object and scene structure that aid latent-parameter decoding and recognition.
- Predictive coding enables future-frame prediction in both synthetic and natural image sequences.
- Learning to predict object or scene motion improves decoding of latent parameters such as viewing angle and can improve recognition performance.
- Accurate future-frame prediction requires at least an implicit model of scene objects and their allowable movements.
5 APPENDIX
The appendix evaluates PredNet controls and published-model comparisons, then examines extrapolation, steering-angle decoding, and prediction-loss variants across several video datasets.
- Evaluation setup: Table 3 evaluates additional PredNet and CNN-LSTM controls using MSE, SSIM, and PSNR for CalTech Pedestrian prediction after KITTI training.The table reports original-hyperparameter scores and averages across five hyperparameter settings.
- Additional controls: Equalizing CNN-LSTM filter counts worsens performance through overfitting, while passing E0 improves results but remains below PredNet with errors passed at all layers.Splitting activations into positive and negative populations does not improve the CNN-LSTM encoder-decoder.
- Published-model comparisons: PredNet outperforms Brabandere et al. on a similar car-camera dataset and outperforms a Mathieu et al.-like model on Human3.6M, but underperforms Finn et al. without hyperparameter optimization.The comparisons use matched PredNet L0 hyperparameters, retrained from scratch with L2 loss.
- Multi-frame prediction: Recursive multi-frame extrapolation produces reasonably accurate near-term predictions but naturally breaks down farther into the future.The appendix contrasts ordinary next-frame prediction with recursive prediction and a model fine-tuned for extrapolation.
- Steering-angle estimation: PredNet representations support steering-angle estimation whose accuracy improves with more input frames and remains reasonable after only one time step.The readout concatenates representations from all layers after spatial pooling and is evaluated with cross-validation.
- Loss placement: 3% and 6%: the PredNet Lall model underperforms the L0 model in MSE on the rotating-faces and CalTech Pedestrian datasets, respectively.Both models produce visually similar next-frame predictions, despite Lall applying prediction loss at all layers.