Stochastic Variations for Latent Data Representation (SVLDR): A Probabilistic Generative Modeling Framework

This paper introduces an innovative enhancement method to Variational Autoencoders (VAEs) by integrating uncertainty into the latent representations, enabling the generation of more diverse and realistic data samples. Traditional VAEs often face limitations in exploring the latent space, leading to less variability in generated outputs. To address this, the proposed method incorporates controlled uncertainty in the latent space, facilitating a more comprehensive exploration of potential data configurations. Additionally, we propose a novel two-stage approach where a small neural network, trained on synthetic data drawn from a standard normal distribution, transforms the uncertain latent representation into a Gaussian prior. This transformation enhances the fidelity of the latent space, improving the quality of the generated samples. The effectiveness of this approach is demonstrated using the MNIST dataset, a benchmark for generative modeling, and evaluated using the Negative Log Likelihood (NLL) metric thereby assessing both the quality and diversity of generated samples. This method has broad applications, including data augmentation, anomaly detection, semi-supervised learning, and generating diverse samples for creative tasks.