Citation :

AnNing, Mazida Ahmad, Huda lbrahim, 2024. "Variational Autoencoders: A Deep Generative Model for Unsupervised Learning" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 1: 59-64.

Abstract :

Variational Autoencoders (VAEs) have become a popular deep generative model for unsupervised learning. This paper aims to investigate the effectiveness of VAEs in learning latent representations and generating meaningful samples. By leveraging the recognition and generative models in VAEs, a variational lower bound on the data log-likelihood can be optimized through backpropagation. Through experiments on a variety of datasets, including MNIST and CIFAR-10, it is demonstrated that VAEs can capture complex latent structures and generate high-quality samples with diverse variations. Furthermore, the learned latent representations exhibit desirable properties such as disentangling factors of variation. In conclusion, VAEs have shown great promise as a deep generative model for unsupervised learning, offering a powerful tool for various applications in computer vision and natural language processing.

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Keywords :

Variational Autoencoders, Deep Generative Model, Unsupervised Learning.