Computer Vision - Applications in Generative Models and Trends

The applications of generative adversarial networks (GANs) and the modern trends in deep learning for computer vision are the core components of this course. It begins by illustrating the modifications of GANs. You discover the role of discriminators in identifying the reality and fakeness of the tuple. This component includes the upgraded techniques and applications of GANs. Next, the notion of domain translation in computer vision is explained. You explore the process of creating a pattern similar to the data represented in the target domain based on the input from the source domain. Generating images across multiple domains using generative adversarial networks follows. Study the various associations of disentanglement with variational autoencoders. You will then discover the process of exposing extricated latent space and obtaining the capabilities of disentanglement and reconstruction.

Next, the course illustrates the use of deep generative models for revising videos and images. You study the procedure for editing images using invertible conditional generative adversarial networks. In addition, the significance of scene dynamics and pose futures in generating and forecasting videos is described. The concepts of zero-shot and few-short learning come next. You will determine the procedures for building recognition models for concealed and unlabelled target classes using zero-shot learning methods. Subsequently, the process of forecasting the outcomes based on restricted samples using few-short learning is illustrated. Explore the taxonomy of methods that constrain hypothesis, augment training, and alter search strategy using prior knowledge. The course works through the concept of self-supervised learning in computer vision. Discover how computers are trained to accomplish tasks without the help of humans in tagging data. The procedure of drawing significant representations of downstream tasks is also described.

Finally, you will learn the methods of yielding labels by exploiting a plethora of unlabelled data using self-supervised learning processes. The procedures for predicting the adversarial robustness using probability distributions are highlighted. You will comprehend the limitations of self-supervised learning and how they are vulnerable to various malicious attacks. Explore the method for establishing adversarial defence mechanisms for enhancing the strength of neural networks by training them with various kinds of adversarial trials. Following this, the multiple methods of model compression are discussed. Discover the process of compressing a model without compromising its precision and performance including the methods of eliminating redundant parameters to speed up the performance. Lastly, you will learn the essential skills required for constructing optimal neural networks that replace hand-made models. If studying the different methods involved in transforming information from images and discovering ways of shielding the learning models from various kinds of threats enthrals you, this course is for you. Don’t delay: sign up today!