# Interpretation for Variational Autoencoder Used to Generate Financial Synthetic Tabular Data
## 5 Discussion  
Through four experiments, the sensitivity-based method shows three major advantages compared with the visualization method mentioned in Section 2. First, the method can intuitively demonstrate feature importance and interaction numerically and graphically. Second, the experimental results prove that the proposed approach can provide both local and global interpretations. Third, the method is computationally inexpensive. The computational time to calculate either feature importance (first-order partial derivatives) or feature interaction (second-order partial mixed derivatives) in the application examples is on the order of seconds on a Google cloud platform. Although computational time will increase as the dataset size increases, this sensitivity-based method is still expected to explain the impacts of features on the latent space of a VAE in a very efficient way. However, this sensitivity-based method also has some restrictions. One limitation is that after calculating the sensitivity, relative importance, and feature interaction, data scientists still need to use their domain knowledge and experience to determine threshold values for the first and second derivatives to eliminate features. Another limitation is this sensitivity-based method heavily relies on the training of a VAE, unlike TabNet which can be used as a preprocessing step to identify important features. The proposed method requires fine-tuning of VAE hyperparameters for accurate interpretations. If the latent space of a VAE fails to generalize well and extract the key information from an input dataset, interpretations may not be able to provide any useful information. In addition, the proposed method may generate different results due to factors such as random initialization, data variability and VAE hyperparameter tuning. However, these differences can be minimized by using a fixed seed for initializing the weights, stabilizing the random selection of data samples in mini-batch updates, and maintaining consistent VAE hyperparameters..  
As emphasized in Section 1, the main objective of this paper was to study the impact of input features in an original tabular dataset on learning the latent representation of the tabular dataset in a VAE model, which is used to generate synthetic data. This paper concentrates on extending sensitivity analysis to assess the contributions of different features in an input dataset to the latent representation of a VAE. Below are some future work proposals. Interpretations will have practical meanings only when a VAE model has a relatively good performance. A well-learned latent space makes interpretation results more meaningful. In the application example, the loss value may be further reduced by tuning hyperparameters to train a better latent representation. To ensure a good VAE model performance before implementing interpretability techniques, one potential research direction could be to investigate if there are any possibilities for designing a better loss function to describe the performance of a VAE model. Figure 20: Global feature interaction values for dataset 3. Since financial data usually contain sensitive information, using synthetic data to replace real data is becoming more favorable within banks. VAE is one of the most popular deep-learning models for synthesizing data [2]. Understanding how VAE generates synthetic data is important. In this context, apart from feature importance calculated by sensitivity analysis introduced in this paper, more tools or methodologies can be designed to provide additional explanations of synthetic data generation using a VAE model in the future. The proposed method in this paper can be expanded to other industries beyond finance, such as education and healthcare, where VAE can be utilized to generate synthetic data for protecting the privacy of students and patients and obtaining more training data.