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

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

How does the choice of activation function in the generator impact the stability of GAN training?Question 21Answera.It can speed up the convergence speed but not stabilityb.It has no impact on stabilityc.It can even lead to vanishing gradients and instability due to slow convergenced.It ensures smooth gradients and stable training

Do you have experience with GANs/Diffusion based models such as Stable Diffusion, Midjourney etc?*Your answer

So far, I’ve written about three types of generative models, GAN, VAE, and Flow-based models. They have shown great success in generating high-quality samples, but each has some limitations of its own. GAN models are known for potentially unstable training and less diversity in generation due to their adversarial training nature. VAE relies on a surrogate loss. Flow models have to use specialized architectures to construct reversible transform.