Folded Recurrent Neural Networks for Future Video Prediction (1712.00311v2)

Abstract: Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach.

• The paper's main contribution is the development of bijective GRUs that enable a bidirectional mapping between inputs and outputs.
• It demonstrates that the fRNN architecture improves prediction accuracy while using 2 to 3 times less memory and computational resources.
• The shared encoder-decoder state mechanism enhances explainability and allows efficient layer removal for model debugging and optimization.

Analyzing Folded Recurrent Neural Networks for Future Video Prediction

The paper presents a novel approach to future video prediction through the introduction of Folded Recurrent Neural Networks (fRNNs), leveraging a unique architecture based on bidirectional Gated Recurrent Units (bGRUs). This architectural innovation addresses significant challenges in the domain, such as high variability in video data, temporal error propagation, and non-specificity of future frames. The proposed method not only demonstrates improvements in prediction accuracy but also optimizes computational and memory efficiencies compared to existing methods.

The primary contribution of this work lies in the development of bijective GRUs, extension of standard GRUs, allowing the input to be treated as another recurrent state, updated via an additional set of logic gates. This creates a bijective mapping between inputs and outputs, enabling a bidirectional flow of information through stacked layers. This design results in a recurrent auto-encoder in which the encoder and decoder states are shared. Such an arrangement facilitates stratified representation learning, meaning some information is not passed to the subsequent layers, inherently reducing computational overhead and error propagation during the generation of multiple frames.

The fRNN architecture allows for only the encoder or decoder to be active during encoding or prediction steps, substantially reducing computational costs. Empirical results demonstrate that the proposed approach achieves state-of-the-art results on the MMNIST and UCF101 datasets and competitive performance on the KTH dataset, all while using significantly less memory and computational resources than existing leading methods. Specifically, fRNN uses 2 to 3 times less memory and computation compared to competing techniques that showed the best scored approaches.

In relation to existing literature, fRNN introduces a significant modification to recurrent auto-encoders by innovating beyond common bridge connections found in Video Ladder Networks (VLN) and Recurrent Ladder Networks (RLN). The shared-state mechanism leads to distinct advantages: enhanced explainability through layer removal, improved efficiency by bypassing unnecessary encoder-decoder operations, and alleviated error magnification due to backward decoder operations. The methodology inherently supports an identity function during training, offering stability especially in scenarios prone to model misconvergence like homogeneous background prediction tasks.

Additionally, by deconstructing the network post-training, the paper explores an interesting aspect of model explainability. It facilitates potential analysis of specific layer contributions to representation dynamics. This is exemplified through the success in removing trained layers without a loss of core functionality in predictions, which aids in model debugging and efficiency optimization.

In conclusion, the paper's fRNN method for future video prediction introduces not only a high-performance model but one which simplifies the computational demands associated with recurrent neural networks. These advancements offer promising implications for future developments in video prediction tasks, particularly those leveraging extensive unlabeled datasets in an unsupervised learning framework. The proposed architecture represents a step forward in making such models more interpretable, efficient, and accessible for practical applications. Future research might explore further refinements in state-sharing strategies and broader applicability across diverse video prediction scenarios beyond the datasets discussed.