Generative Adversarial Networks (GANS) can be broken down into parts A-1 В-2 С-3 D-4 is used To learn a generative model, which describes how data is generated in terms of a probabilistic model...

Extracted text: Generative Adversarial Networks (GANS) can be broken down into parts A-1 В-2 С-3 D-4 is used To learn a generative model, which describes how data is generated in terms of a probabilistic model A-Adversarial B-Generative C-Networks D-discriminator Which is one of the most popular also the most successful implementation of GAN? A-Conditional GAN B-Vanilla GAN C-Deep Convolutional GAN D-Laplacian Pyramid GAN Q2 : cite some application examples of GANS?


Extracted text: Q1 : choose the right answer -Operations Research approach is A-multi-disciplinary B-scientific C-intuitive D-collect essential data -A feasible solution to a linear programming problem A-must satisfy all the constraints of the problem simultaneously B-need not satisfy all of the constraints, only some of them C-must be a corner point of the feasible region D-must optimize the value of the objective function -A metaheuristic approach generates A-Optimal solution B-approximate solution C-realisable solution D-feasible solution -GAN Stands for A-Generative Advertising Network B-Generative Adversarial Network C-Generate Adversarial Network D-Generation adversarial Network -Generative Adversarial Networks was developed and introduced by A-Allan Tunning B-J. Goodfellow C-Rutherford D-None of the above Generative Adversarial Networks was developed and introduced in A-2015 B-2014 C-2013 D-2012