Generative Adversarial Networks
After Deep Autoregressive Models, Deep Generative Modelling and Variational Autoencoders we now continue the discussion with Generative Adversarial Networks (GANs).
Entries by Sunil Yadav
Variational Autoencoders
ariational autoencoders (VAEs) are a deep learning method to produce synthetic data (images, texts) by learning the latent representations of the training data. AGMs are sequential models and generate data based on previous data points by defining tractable conditionals.
Deep Autoregressive Models
In this blog article, we will discuss about deep autoregressive generative models (AGM). Autoregressive models were originated from economics and social science literature on time-series data where obser- vations from the previous steps are used to predict the value at the current and at future time steps
Deep Generative Modelling
Nowadays, we see several real-world applications of synthetically generated data, for example solving the data imbalance problem in classification tasks, performing style transfer for artistic images, generating protein structure for scientific analysis, etc. In this blog, we are going to explore synthetic data generation using deep neural networks with the mathematical background.
