PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning

Abstract: The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical context, where the visual dynamics are believed to have modular structures that can be learned with compositional subsystems. This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment. Concretely, besides the original memory cell of LSTM, this network is featured by a zigzag memory flow that propagates in both bottom-up and top-down directions across all layers, enabling the learned visual dynamics at different levels of RNNs to communicate. It also leverages a memory decoupling loss to keep the memory cells from learning redundant features. We further propose a new curriculum learning strategy to force PredRNN to learn long-term dynamics from context frames, which can be generalized to most sequence-to-sequence models. We provide detailed ablation studies to verify the effectiveness of each component. Our approach is shown to obtain highly competitive results on five datasets for both action-free and action-conditioned predictive learning scenarios.

• The paper presents a novel ST-LSTM architecture that uses dual memory cells and a zigzag memory flow to effectively capture both short- and long-term dynamics.
• The paper employs a reverse scheduled sampling strategy, enhancing long-term dependency learning and improving prediction accuracy on sequential data.
• The paper demonstrates significant performance gains on datasets like Moving MNIST and KTH Actions, outperforming conventional ConvLSTM models in key metrics.

Overview of PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning

The paper presents PredRNN, a novel recurrent neural network designed to address the challenges of spatiotemporal predictive learning. Traditional approaches often use ConvLSTM models to predict future frames in sequences; however, such models can struggle with simultaneously capturing spatial and temporal dynamics due to limitations in their memory state transitions.

Key Contributions

Spatiotemporal Memory Flow: PredRNN introduces a zigzag memory flow that deviates from the conventional horizontal memory transitions seen in ConvLSTM. This approach allows memory states to propagate bottom-up and top-down through different layers, facilitating the interaction between hierarchical visual features.

Spatiotemporal LSTM (ST-LSTM): The central element of PredRNN is the ST-LSTM, which employs a dual memory mechanism with two distinct memory cells: one for horizontal temporal transitions and another for vertical spatiotemporal ones. This decoupling enables focused modeling of both short-term and long-term dynamics, improving the network’s ability to anticipate complex variations in spatiotemporal sequences.

Memory Decoupling: Recognizing that intertwined memory states can lead to inefficient feature learning, the authors propose a decoupling loss function that maximizes the diversity and separation between the ST-LSTM’s memory cells. This ensures comprehensive coverage of dynamic patterns.

Reverse Scheduled Sampling: To enhance long-term dependency learning, the paper introduces a curriculum learning strategy that varies the input sampling during training, forcing the model to rely progressively on past observations.

Empirical Validation

PredRNN shows remarkable improvements across several challenging datasets, including Moving MNIST, KTH Actions, and real-world datasets like Traffic4Cast and radar echoes for precipitation forecasting. The model performs well in both action-free and action-conditioned scenarios, notably using action-modulated ST-LSTM units to make context-aware predictions.

Quantitative Results

• On the Moving MNIST dataset, PredRNN achieves substantial reductions in mean squared error (MSE), outperforming ConvLSTM and other competitive models. For example, PredRNN-V2 reduces MSE to 48.4 compared to ConvLSTM’s 103.3.
• For the KTH Action dataset, PredRNN yields a SSIM of 0.839 against ConvLSTM’s 0.712, indicating enhanced structural similarity preservation in its predictions.
• In traffic prediction scenarios, incorporating PredRNN into an autoencoder structure like U-Net results in significant performance gains, as demonstrated on the Traffic4Cast dataset.

Implications and Future Work

The architecture of PredRNN balances the necessity of handling both spatial and temporal complexities, which is crucial for future advancements in predictive modeling. The implementation of the spatiotemporal memory flow and decoupling mechanisms can potentially inspire developments in unsupervised learning tasks beyond sequence prediction, such as reinforcement learning and video understanding.

Future developments may focus on optimizing PredRNN for even larger and more diverse datasets, potentially incorporating external knowledge sources such as environmental physics for tasks like weather forecasting. Extending the reverse scheduled sampling approach to other sequence-to-sequence learning models may enhance generalization across different domains.

Overall, while the paper does not claim groundbreaking innovation, it provides a rigorous advancement in handling the intricacies of spatiotemporal predictive tasks, highlighting PredRNN as a versatile and robust architecture for complex sequence modeling challenges.