Z-Forcing: Training Stochastic Recurrent Networks (1711.05411v2)

Abstract: Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks (RNN). Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech. We unify successful ideas from recently proposed architectures into a stochastic recurrent model: each step in the sequence is associated with a latent variable that is used to condition the recurrent dynamics for future steps. Training is performed with amortized variational inference where the approximate posterior is augmented with a RNN that runs backward through the sequence. In addition to maximizing the variational lower bound, we ease training of the latent variables by adding an auxiliary cost which forces them to reconstruct the state of the backward recurrent network. This provides the latent variables with a task-independent objective that enhances the performance of the overall model. We found this strategy to perform better than alternative approaches such as KL annealing. Although being conceptually simple, our model achieves state-of-the-art results on standard speech benchmarks such as TIMIT and Blizzard and competitive performance on sequential MNIST. Finally, we apply our model to LLMing on the IMDB dataset where the auxiliary cost helps in learning interpretable latent variables. Source Code: \url{https://github.com/anirudh9119/zforcing_nips17}

• The paper introduces Z-Forcing, integrating latent variables into an autoregressive RNN framework to enhance generative modeling of sequential data.
• It employs a backward-running RNN for amortized variational inference, effectively capturing future sequence dependencies.
• Experimental results on speech and MNIST datasets demonstrate competitive performance and promise for diverse sequential tasks.

Overview of Z-Forcing: Training Stochastic Recurrent Networks

The paper proposes a novel method, termed Z-Forcing, to train stochastic recurrent networks for generative modeling of sequential data using autoregressive decoders—specifically focusing on recurrent neural networks (RNNs). Stochastic recurrent models are well-regarded for their ability to capture variability in sequential data, and Z-Forcing integrates existing successful techniques into a single coherent framework with several novel elements.

The paper tackles the inherent challenges in recurrent models which traditionally rely on deterministic hidden state evolution, presenting limitations in modeling highly structured sequences. The authors introduce stochastic latent variables associated with each timestep, which interact with the recurrent dynamics, and employ amortized variational inference with a backward-running RNN to improve latent variable training efficacy.

Methodology and Architecture

The core contribution of Z-Forcing lies in the design of the generative and inference models:

1. Generative Model: The methodology leverages latent variables integrated with the autoregressive RNN, enhancing the capacity to capture future dependencies and output multi-modal distributions. This framework contrasts with simpler deterministic hidden state models and enables complex prediction tasks, with Gaussian latent variables conditioning the hidden dynamics.
2. Inference Model: A backward RNN is employed to craft the approximate posterior of these latent variables, informed by the future sequence—a step paralleling the SRNN model but incorporating the latent variables in autoregressive dynamics. This technique facilitates inferences that account for the future observations, marking a deviation from traditional approaches where inference models might not encompass sequential foresight.
3. Auxiliary Cost: Recognizing challenges with latent variable utilization, particularly when embedding strong autoregressive models, Z-Forcing introduces an auxiliary task. This task involves reconstructing the backward network’s state and is designed to enrich the learning signal for latent variables, impelling them to store and encode meaningful data beyond local pixel correlations.

Experimental Results

The effectiveness of Z-Forcing is confirmed through experiments with speech datasets, Blizzard and TIMIT, as well as sequential MNIST data. On speech datasets, Z-Forcing outperforms previous models, showcasing enhanced log-likelihood results. On sequential MNIST, the method demonstrates competitiveness with state-of-the-art results while preserving model simplicity. In the domain of LLMing, particularly the IMDB dataset, the auxiliary cost aids the model in capturing latent language structure, allowing for sentence interpolation.

Theoretical Implications and Future Directions

The implications of this research are profound, both theoretically and practically. The integration of stochastic elements in RNNs opens new avenues for modeling sequences with inherent multimodality and complex structures. Practically, Z-Forcing is poised to contribute towards improved synthesis in fields like speech generation, LLMing, and potentially areas demanding interpretable generative models.

In future developments, enhancing the architectural complexity by integrating more powerful autoregressive models like PixelRNN/PixelCNN, or exploring multitask learning applications, could further leverage the latent variable framework presented in Z-Forcing.

Conclusion

The Z-Forcing paper demonstrates an innovative approach to the integration of stochastic elements in RNN architectures, providing substantive improvements over traditional methods in modeling sequential data. The authors effectively unify and augment existing concepts to develop a powerful generative model with promising applications in various domains dealing with sequential data.