Generative Adversarial Networks
After Deep Autoregressive Models, Deep Generative Modelling and Variational Autoencoders we now continue the discussion with Generative Adversarial Networks (GANs).
Sunil Yadav is an experienced researcher with a keen focus on applying academic research to solve real-world problems. He believes a research paper has more value if it can be used for the welfare of society in general and the wellness of people in particular. He finished his PhD in mathematics and computer science and has a focus on computer vision, 3D data modelling, and medical imaging. His research interests revolve around understanding the visual data and producing meaningful output using the different areas of mathematics, including Deep learning, Machine learning, and computer vision.
After Deep Autoregressive Models, Deep Generative Modelling and Variational Autoencoders we now continue the discussion with Generative Adversarial Networks (GANs).
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.
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