Disentangled Sequential Autoencoder (1803.02991v2)

Abstract: We present a VAE architecture for encoding and generating high dimensional sequential data, such as video or audio. Our deep generative model learns a latent representation of the data which is split into a static and dynamic part, allowing us to approximately disentangle latent time-dependent features (dynamics) from features which are preserved over time (content). This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features. In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping. For audio, this allows us to convert a male speaker into a female speaker and vice versa, while for video we can separately manipulate shapes and dynamics. Furthermore, we give empirical evidence for the hypothesis that stochastic RNNs as latent state models are more efficient at compressing and generating long sequences than deterministic ones, which may be relevant for applications in video compression.

• The paper presents a novel VAE that separates static content from dynamic variations in sequential data.
• It employs a stochastic RNN to efficiently encode, compress, and generate long sequences.
• Experiments on cartoon videos and audio demonstrate its potential for precise content manipulation and style transfer.

Overview of Disentangled Sequential Autoencoder

The paper "Disentangled Sequential Autoencoder" introduces a novel Variational Autoencoder (VAE) architecture focused on encoding and generating high-dimensional sequential data, such as video and audio. This model specifically isolates latent factors into static (content) and dynamic (dynamics) parts, which enables the disentanglement of time-dependent features from those preserved over time. Such disaggregated learning models have shown significant applications in content manipulation and style transfer, as evidenced by experiments with artificially generated cartoon video sequences and audio recordings.

Model Structure and Methodology

The proposed architecture employs separate latent variables for static and dynamic representations, facilitating distinct handling and manipulation of content and dynamics in sequences. The content is modeled by a latent variable that remains steady across the sequence, while the dynamics are encapsulated by variables that differ with each frame. This VAE framework allows the model to generate sequences with new combinations of content and dynamics through feature swapping, which translates to operations such as converting the voice of a speaker in audio data or changing the identity of objects in videos.

In the experimental setup, the authors demonstrate that by using a stochastic RNN structure for latent state modeling, efficient compression and generation of sequence data, especially long sequences, are feasible. This aligns with the hypothesis that stochastic models in generative frameworks outperform deterministic ones in certain tasks such as video compression.

Experimental Validation

The experiments presented utilize both artificially constructed datasets and real-world audio data to establish the effectiveness of the model. On cartoon video datasets, the separation of identities (content) and movements (dynamics) is clearly exemplified as they operate differentially when generating or reconstructing the sequences. The same disentanglement concept is applied to audio, where male and female voices can be altered under fixed content scenarios, testing the model’s robustness and flexibility.

Quantitatively, the model’s ability to preserve time-invariant features while facilitating the manipulation of dynamic properties was evaluated via classification and empirical error measures. The results underscore that generating sequences with fixed content but randomized dynamics— or vice versa—preserves intended attributes while yielding diverse outcomes in the reconstructed sequences.

Implications and Future Directions

The theoretical implications of this research lie in its potential to streamline sequence generation and manipulation in machine learning, extending beyond traditional applications to areas such as neural video encoding and efficient data representation. Practically, the architecture can be adapted for various applications, including speech and video synthesis, where control over content and style is paramount.

Looking ahead, further exploration into integrating discriminative objectives could enhance the disentangled representation further, refining the separation between static and dynamic components. Additionally, adapting the current model to handle more complex datasets without excessive supervision hints at the continuing potential for unsupervised learning methodologies in future AI systems.

In summary, the paper provides a comprehensive methodological framework for disentangled learning in sequential data, showcasing its broad applicability and setting the stage for more intricate developments in representation learning. This approach opens avenues for future research in efficient sequence modeling and manipulation, crucial for advancing machine learning capabilities in high-dimensional data processing.