WGAN implementation from scratch (with gradient penalty)

In this video we implement WGAN and WGAN-GP in PyTorch. Both of these improvements are based on the loss function of GANs and focused specifically on improving the stability of training. Resources and papers: https://www.alexirpan.com/2017/02/22/wasserstein-gan.html https://arxiv.org/abs/1701.07875 https://arxiv.org/abs/1704.00028 ❤️ Support the channel ❤️ https://www.youtube.com/channel/UCkzW5JSFwvKRjXABI-UTAkQ/join Paid Courses I recommend for learning (affiliate links, no extra cost for you): ⭐ Machine Learning Specialization https://bit.ly/3hjTBBt ⭐ Deep Learning Specialization https://bit.ly/3YcUkoI 📘 MLOps Specialization http://bit.ly/3wibaWy 📘 GAN Specialization https://bit.ly/3FmnZDl 📘 NLP Specialization http://bit.ly/3GXoQuP ✨ Free Resources that are great: NLP: https://web.stanford.edu/class/cs224n/ CV: http://cs231n.stanford.edu/ Deployment: https://fullstackdeeplearning.com/ FastAI: https://www.fast.ai/ 💻 My Deep Learning Setup and Recording Setup: https://www.amazon.com/shop/aladdinpersson GitHub Repository: https://github.com/aladdinpersson/Machine-Learning-Collection ✅ One-Time Donations: Paypal: https://bit.ly/3buoRYH ▶️ You Can Connect with me on: Twitter - https://twitter.com/aladdinpersson LinkedIn - https://www.linkedin.com/in/aladdin-persson-a95384153/ Github - https://github.com/aladdinpersson OUTLINE: 0:00 - Introduction 0:27 - Understanding WGAN 6:53 - WGAN Implementation details 9:15 - Coding WGAN 15:50 - Understanding WGAN-GP 18:48 - Coding WGAN-GP 25:29 - Ending