G-NeuroDAVIS: A generative model for data visualization through a generalized embedding

Visualizing high-dimensional datasets through a generalized embedding has been a longstanding challenge. Several methods have been proposed for this purpose, but they have yet to generate a generalized embedding that not only reveals the hidden patterns present in the data but also generates realistic high-dimensional samples from it. Motivated by this aspect, in this article, a novel generative model called G-NeuroDAVIS has been developed, which is capable of visualizing high-dimensional data through a generalized embedding and thereby generating new samples. The model leverages advanced generative techniques to produce high-quality embedding that captures the underlying structure of the data more effectively compared with the existing methods. G-NeuroDAVIS can be trained in both supervised and unsupervised settings. We have rigorously evaluated our model through a series of experiments, demonstrating superior performance in several downstream tasks, which highlights the effectiveness of the learned representations. Results of an interpolation experiment reflect a smooth and meaningful transition in the generated images across various paths, which in turn depict preservation of underlying data structure. Furthermore, the conditional sample generation capability of the model has been described through both qualitative and quantitative assessments, revealing a marked improvement in generating realistic and diverse samples. G-NeuroDAVIS has outperformed Variational Autoencoder (VAE) significantly in terms of embedding quality and downstream tasks like classification. Moreover, the superior sample generation capability of G-NeuroDAVIS has been demonstrated against VAE, Deep Convolutional Generative Adversarial Network (DCGAN), Denoising Diffusion Probabilistic Models (DDPM), and Autoencoder (AE)-guided Real-valued Non-Volume Preserving (RealNVP). These results highlight the efficacy of G-NeuroDAVIS to serve as a robust tool in various applications that demand high-quality data generation and representation learning.