Key Changes:Reduced Model Complexity: The discriminator model now has fewer layers, which speeds up training.Increased Batch Size: Using a batch size of 64 instead of 32 will make each epoch faster.Reduced Epochs: Set epochs to 10,000 instead of 20,000 to save time while still producing good results.These changes should help reduce the time it takes to train your GAN and generate samples, while still providing high-quality synthetic data.

Why might someone want to use GANs on the CIFAR-10 dataset?To delete images from the datasetTo classify the images in the datasetTo critique the images in the datasetTo reduce the size of the datasetTo generate novel and relevant images to augment dataset

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.

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

Which architecture can help address convergence issues in traditional GANs?RNNDBNWGANCNNLSTM

Which technique can help in dealing with training instability in GANs?Noise additionAll of the given optionsGradient clippingData augmentationDropout