9. GANs vs. Diffusion Models Explained | Deep Generative Modeling | In Hindi
About this lesson
Are GANs still relevant, or have Diffusion Models taken over the generative AI world? In this video, we break down the definitive comparison between Generative Adversarial Networks (GANs) and Diffusion Models. What You’ll Learn: The Core Difference: How GANs use adversarial training (Generator vs. Discriminator) while Diffusion Models rely on iterative denoising. Quality vs. Speed: Why GANs are lightning-fast at inference but Diffusion Models generate sharper, more diverse high-fidelity images. Training Stability: We explore why Diffusion Models offer more stable likelihood optimization compared to the often unstable adversarial nature of GANs. Real-World Use Cases: From image synthesis and data augmentation to high-fidelity audio/video tasks. Whether you're a data scientist or an AI enthusiast, this deep dive will help you choose the right model for your next project. Subscribe for more deep dives into Generative AI!
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