Transformer Placement in Variational Autoencoders Achieves Fidelity on 57 Datasets

Scientists are tackling the persistent challenge of generating realistic tabular data, a task where standard Variational Autoencoders (VAEs) often fall short due to difficulties modelling complex feature relationships. Aníbal Silva, Moisés Santos, and André Restivo, from the Universities of Porto, alongside Carlos Soares et al, present research exploring how incorporating Transformer architectures into VAEs can improve performance. Their work investigates the optimal placement of these Transformers within the VAE structure, utilising 57 datasets from the OpenML CC18 suite. This study is significant because it reveals a crucial trade-off between the fidelity and diversity of generated data depending on Transformer placement, and identifies surprising linearity within Transformer blocks, potentially streamlining future generative model designs.

This research addresses the challenges of modelling complex relationships within tabular datasets, particularly those containing mixed data types, where standard multilayer perceptron-based VAEs often struggle. The team achieved this by empirically investigating the impact of strategically placing Transformers within different components of a VAE, ultimately aiming to enhance the generation of synthetic data. Experiments were conducted on 57 datasets sourced from the OpenML CC18 suite, providing a robust evaluation platform for the proposed methodology.

The study reveals a crucial trade-off between the fidelity and diversity of generated data when leveraging Transformers to process latent and decoder representations. Specifically, incorporating Transformers into these areas increases the diversity of synthetic data but potentially reduces its faithfulness to the original data distribution. This finding highlights the nuanced interplay between these two critical aspects of generative modelling. Furthermore, the research establishes a significant observation regarding the behaviour of Transformers within the VAE architecture: consecutive blocks exhibit a high degree of similarity, and in the decoder, the relationship between input and output is approximately linear.
This breakthrough unveils that the Transformer, when implemented in the decoder, functions almost as an identity function, due to the effects of layer normalization which shifts and scales initial representations, resulting in minimal representational changes. Researchers utilized Center Kernel Alignment (CKA) to meticulously compare feature representations across different architectural components, providing a deeper understanding of how information flows and transforms within the VAE. The work opens avenues for refining generative models for tabular data, potentially improving data augmentation techniques, addressing data scarcity issues, and enhancing individual privacy preservation. The research establishes that Transformers are becoming a fundamental architectural block for modelling feature interactions in tabular data across various learning paradigms. By questioning the conventional use of Transformers at the raw data input level, the team explored their potential to leverage abstract representations within a VAE, specifically focusing on the encoder, latent space, and decoder. This evaluation, encompassing six distinct VAE variations, was performed using metrics assessing both the statistical properties of synthetic data and its utility in machine learning tasks, providing a comprehensive assessment of the proposed approach.

Tabular Data Tokenisation and VAE Integration offers promising

Scientists investigated the impact of integrating Transformer architectures into Variational Autoencoders (VAEs) for tabular data generation. The study employed 57 datasets from the OpenML CC18 suite, meticulously examining the trade-offs between fidelity and diversity in generated data. Researchers engineered a feature tokenization process to represent mixed-type tabular data, numerical and categorical features, as continuous vectors within a shared embedding space. This involved projecting numerical features using learnable weights and biases, while categorical features underwent a lookup table transformation, creating an embedding matrix E of dimension RM×d.

The team developed a feature detokenizer to reconstruct data from the embedding space, projecting each embedding vector back into the original feature space using learned parameters. This reconstruction process utilized a Softmax function for categorical features to ensure valid one-hot encoded outputs. Crucially, the study pioneered the application of dot-product attention mechanisms, central to the Transformer architecture, to capture relationships between variables in the embedding space. Attention(Q, K, V) = Softmax QKT/dk V was implemented, where Q, K, and V represent query, key, and value matrices derived from the embedded data, and dk denotes the embedding dimensionality.

Experiments systematically positioned Transformers within different components of the VAE, latent and decoder representations, to assess their influence on data quality. The research revealed a consistent pattern of high similarity between consecutive Transformer blocks across all components, particularly within the decoder. Analysis demonstrated that the relationship between the input and output of a Transformer block in the decoder was approximately linear, suggesting efficient information flow. This innovative approach enables a nuanced understanding of how Transformers affect tabular data generation, offering insights into optimizing VAE architectures for improved performance and generative capabilities. The research focused on understanding how positioning Transformers within different components of a VAE impacts both the fidelity and diversity of generated synthetic data. Results demonstrate a clear trade-off between these two qualities; incorporating Transformers generally increases diversity but can reduce fidelity to the original data distribution. The most significant gains in diversity were achieved when Transformers were applied to both latent and decoder representations within the VAE architecture.

Experiments revealed a high degree of similarity between consecutive blocks of the Transformer in all components of the VAE. Specifically, analysis of the decoder showed an approximately linear relationship between the input and output of the Transformer, suggesting a near-identity function effect. Measurements confirm this linearity, indicating minimal representational changes within the residual connections of the decoder. Researchers attribute this phenomenon to layer normalization, which shifts and scales the initial representation, effectively limiting the Transformer’s ability to induce substantial changes.

The team measured statistical properties of the synthetic data against the real data, alongside assessing machine-learning utility to evaluate performance. Center Kernel Alignment (CKA) was employed to compare feature representations across different architectural components, providing insights into how information is transformed throughout the VAE. Data shows that leveraging Transformers on latent and output representations yielded the greatest increase in diversity, while simultaneously demonstrating the trade-off with fidelity. Further analysis of the decoder’s Transformer blocks revealed that the input and output representations exhibited a high degree of similarity. This finding suggests that, in this specific component, the Transformer primarily functions as a scaling and shifting operation due to the influence of layer normalization. The study’s findings provide a detailed understanding of how Transformers interact with VAEs in the context of tabular data, offering valuable insights for future generative model design and optimization.

Transformers balance fidelity and diversity in VAEs

Scientists have investigated the integration of Transformer architectures into Variational Autoencoders (VAEs) for improved generative modelling of tabular data. Their research addresses the challenges of modelling complex relationships between features, particularly within datasets containing both continuous and discrete variables. Experiments conducted on 57 datasets from the OpenML CC18 suite demonstrate that strategically positioning Transformers within a VAE, specifically to utilise latent and decoder representations, results in a trade-off between the fidelity and diversity of generated data. Researchers also observed a notable consistency between consecutive blocks within the Transformer architecture across all components of the VAE.

Notably, the relationship between input and output within the Transformer decoder appears approximately linear. This suggests a potential simplification in how Transformers process information at this stage. The authors acknowledge that the observed linear relationship within the decoder warrants further investigation and may indicate limitations in the model’s capacity to capture highly non-linear interactions. Future work could explore alternative decoder designs or training strategies to address this. These findings contribute to a better understanding of how Transformer architectures can be effectively incorporated into VAEs for tabular data generation, offering insights into the balance between generating realistic and diverse synthetic datasets.