Clockwork Variational Autoencoders (2102.09532v2)

Abstract: Deep learning has enabled algorithms to generate realistic images. However, accurately predicting long video sequences requires understanding long-term dependencies and remains an open challenge. While existing video prediction models succeed at generating sharp images, they tend to fail at accurately predicting far into the future. We introduce the Clockwork VAE (CW-VAE), a video prediction model that leverages a hierarchy of latent sequences, where higher levels tick at slower intervals. We demonstrate the benefits of both hierarchical latents and temporal abstraction on 4 diverse video prediction datasets with sequences of up to 1000 frames, where CW-VAE outperforms top video prediction models. Additionally, we propose a Minecraft benchmark for long-term video prediction. We conduct several experiments to gain insights into CW-VAE and confirm that slower levels learn to represent objects that change more slowly in the video, and faster levels learn to represent faster objects.

• The paper introduces a hierarchical latent dynamics model, Clockwork VAE, which efficiently captures both long-term and short-term dependencies in video sequences.
• It leverages multi-scale temporal abstractions using distinct 'clock speeds' to achieve robust predictions over sequences of up to 500 frames, outperforming models like SVG-LP and RSSM.
• The model adaptively distributes information across its latent hierarchy, demonstrating superior empirical performance on metrics such as SSIM, PSNR, and FVD.

Clockwork Variational Autoencoders for Video Prediction

The paper presents a novel approach to video prediction through the development of the Clockwork Variational Autoencoder (CW-VAE). This model addresses a critical challenge in video prediction: accurately forecasting long-term dynamics while efficiently handling high-dimensional data sequences. The proposed CW-VAE stands out by leveraging a hierarchically structured latent space where each level operates at a distinct temporal abstraction, termed as 'clock speeds'. This stratification enables the model to effectively learn and represent both high-frequency and low-frequency components of video sequences.

Core Contributions

1. Hierarchical Latent Dynamics: CW-VAE employs a multi-level latent structure where top levels tick at slower rates compared to lower levels. This design captures long-term dependencies by allocating different temporal scales to each hierarchy, thus abstracting slower-changing components to higher levels and faster-changing elements to lower levels.
2. Empirical Superiority: The effectiveness of the CW-VAE is empirically validated across four diverse datasets, including the newly proposed Minecraft Navigate dataset, which tests the model's capacity for long-term prediction over sequences of up to 500 frames. CW-VAE outperforms existing state-of-the-art models like SVG-LP, VTA, and RSSM on several metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Frechet Video Distance (FVD).
3. Long-term and Short-term Predictions: Unlike temporally autoregressive models that often suffer from accumulated prediction errors, CW-VAE's latent dynamics predict sequences without needing to reintegrate generated frames. This approach results in robust long-term predictions, effectively demonstrated in scenarios like the MineRL Navigate dataset, where CW-VAE maintained accurate trajectories for up to 400 frames.
4. Adaptive Representation: The design allows CW-VAE to adaptively distribute information across its hierarchical levels depending on the speed of video sequence inputs. Empirical analysis shows that with higher frame rates, more information is naturally offloaded to quicker levels, while slower sequences are modeled using the higher levels.

Experimental Insights

The paper provides comprehensive experimental insights into the model behaviors. It visualizes how different levels of the hierarchy learn and encode information; for example, higher levels hold slower-evolving, broader spatial features, while lower levels focus on immediate, rapid dynamics. The qualitative analysis involving resetting higher levels during prediction further reveals clear separations in content captured at each hierarchy, validating the multi-scale nature of the CW-VAE model.

Future Implications

The findings and framework of the CW-VAE suggest potential applications beyond video prediction. The hierarchical abstraction could improve areas such as reinforcement learning, where understanding long-term dependencies is crucial. Specifically, its latent-driven forward prediction mechanism can be beneficial for planning and control tasks in complex environments.

Moreover, the introduction of benchmarks accommodating longer temporal sequences pushes the frontier for evaluating predictive models. Future research can explore the integration of richer contextual information or extending this approach to other domains, like NLP, where temporal abstraction might also capture linguistic structures across large corpora.

Overall, CW-VAE exemplifies a strategic advancement in variational autoencoders, providing a scalable solution to long-range video prediction and opening avenues for further investigations into temporally hierarchical structures.