Wasserstein GAN with Gradient Penalty in TensorFlow | Image Generation with TensorFlow | GAN 09
Welcome to our course on Gan(Generative Adversarial Networks) or Image Generation with Neural Networks using TensorFlow. In this video, we'll talk about Wgan or Wasserstein Generative Adversarial Networks and implement it using the Python TensorFlow framework. We'll talk about the limitations of Standard GANs, and compare Jensen Shannon divergence with Wasserstein distance, then we'll talk about the features of Earth Mover distance and finally close out the discussion with the Lipschitz constraint and how to implement it using Gradient penalty. GANs are the backbone of modern Generative AI that helps us generate new images and paintings. Gans are also used for image translation, style transfer, and video, and audio generation. This course will cover topics like GANs ( generative adversarial networks ), autoencoders, variational autoencoders, style transfer, and generative AI in TensorFlow. Get ready to dive into the world of image generation using neural networks!
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