Data Generation using Variational Autoencoder...

Data Generation using Variational Autoencoders with Different Datasets

Author Name : Shibu V, Sunitha Manoharan

ABSTRACT Variational autoencoders (VAEs) are a type of deep learning model that can be used for generative modelling. This paper explores the application of Variational Autoencoders for the task of data generation. The key idea behind VAEs is to learn a latent space representation of the data, where each point in this space corresponds to a unique data sample. By sampling from this latent space and decoding these samples, VAEs can generate data that closely resembles the training data. This abstract serves as an introduction to the exciting developments in digit generation using variational auto encoders and provides a foundation for further exploration in this rapidly evolving field. VAEs have been shown to be effective for generating a variety of types of data, including images, text, and audio. In this paper, we propose a new approach to data generation using VAEs. Our approach uses a VAE to learn a latent space representation of data. Once the latent space representation has been learned, we can generate new data samples by sampling from the latent space and decoding the samples back to the original image space. We evaluate our approach on a benchmark dataset of handwritten digits and MNist -fashion and then show that it achieves state of-the-art results on this task. Our approach also has the advantage of being able to generate realistic and diverse data samples. VAEs can generate realistic and diverse data images, and they can be used for a variety of applications.