An Introduction to Generative Models in Computer Vision

This course illustrates the importance of developing mathematical models for performing various computer vision tasks. It begins by illustrating the notion of different generative models in deep learning. You will study the landscape of machine learning and ascertain the importance of generative models in the scheme of deep learning methods. This section includes the procedure for computing the patterns in a dataset using unsupervised learning methods. See how generative adversarial networks (GANs) use two different networks to generate new data identical to several data models in the training datasets. In addition, the process of training and evaluating various types of generative adversarial networks are explained. You will comprehend the significance of variational autoencoders (VAEs) in deep generative models. The multiple applications and the procedure for training these autoencoders using computer vision techniques are described. 

Variational autoencoders are used in dealing with latent space irregularity. This course explains this concept including the process of determining the parameters of the distribution using Gaussian models. You will be taught about the various methods that unify various deep generative models using adversarial networks and variational autoencoders.Explore the process of mapping the generated images of GAN using VAE to improve the standard. This process comprises the procedure for enhancing the quality of the image by integrating variational autoencoders and generative adversarial networks. The process of normalizing the latent code using an adversarial loss mechanism is discussed including using classifiers to distinguish between original data and created data anticipated by the generative network. Subsequently, you will study how VAE-GAN outclasses conventional variational autoencoders. Replacing the decoder of VAEs using a GAN discriminator for ascertaining the loss function is explained. 

Finally, the procedure for enhancing the quality of the image by integrating variational autoencoders and generative adversarial networks is described. Next, the various deep generative models that have been successful over the years are highlighted. Discover the process of modelling and transforming complex densities using non-linear independent components estimation and real-valued non-volume preserving methods. Following this, the process of converting a simple distribution into a multifaceted one is described including the use of flow based-models to model a probability distribution by influencing the normalising flow. Subsequently, integrating animated alterations with manageable variations in probability distributions using autoregressive flows is described. Lastly, the course explains the various methods of estimating the pixels across its axes and the process of choosing a distribution and its conforming loss function to model the output pixels. Study the fascinating ways of training extremely complicated distributions to predict the forthcoming sequence or patterns using large samples in a dataset.