In a Generative Adversarial Network (GAN) training session, the discriminator evaluates 800 samples. Out of these, it correctly identifies 680 samples as real and 100 samples as fake. What is the discriminator’s accuracy in percentage?

What does the discriminator do in a GAN?Creates imagesEnhances image resolutionEvaluates if an image is real or fakeCombines imagesBoth create and evaluate images

In Conditional GANs (cGANs), what additional information is provided to both the generator and discriminator during training?Question 20Answera. Image gradientsb.Noise vectorc.Latent space vectorsd.Class labels

In the formulation of Generative Adversarial Networks (GANs), which term represents the objective of the generator to match the distribution of generated samples to that of real data?Question 8Answera.Generator lossb.Adversarial lossc. Reconstruction lossd.Discriminator loss

ou are evaluating a binary classifier. There are 50 positive outcomes in the test data, and 100 observations. Using a 50% threshold, the classifier predicts 40 positive outcomes, of which 10 are incorrect.What is the classifier’s Precision on the test sample?1 point25%60%75%80%

In a GAN (Generative Adversarial Network), the network is responsible for generating new data, while the network tries to distinguish between real and generated data.