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Computer Vision - Applications in Generative Models and Trends

Learn about the ever-growing variants of generative adversarial networks and their uses with this free online course.

Publisher: NPTEL
Did you know that there are ways to create realistic images just based on labelled sketches? It is remarkable, right? This course illustrates the process of establishing substantial communication by mapping images between two domains for translating their content. Study the ways of tackling the failures of learning models using adversarial defence mechanisms and the process of ascertaining the optimal neural network design.
Computer Vision - Applications in Generative Models and Trends
  • Duration

    5-6 Hours
  • Students

  • Accreditation






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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 generative adversarial networks. You will discover the role of discriminators in identifying the reality and fakeness of the tuple. This component will include the upgraded techniques and the recent applications of generative adversarial networks. Next, the notion of domain translation in computer vision is explained. You will 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. You will 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 will study the procedure for editing images using invertible conditional generative adversarial networks. In addition to this, the significance of scene dynamics and pose futures in generating and forecasting videos is described. The concepts of zero-shot and few-short learning comes next. You will determine the procedures for building recognition models for concealed and unlabeled target classes using zero-shot learning methods. Subsequently, the process of forecasting the outcomes based on restricted samples using few-short learning is illustrated. You will explore the taxonomy of methods that constrain hypothesis, augment training, and alter search strategy using prior knowledge. Furthermore, the course will work through the concept of self-supervised learning in computer vision. You will 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 unlabeled 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. You will explore the procedure 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. You will discover the process of compressing a model without compromising its precision and performance. This includes 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 and sign up today!

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