Diffusion Models: The AI Behind DALL-E & Sora скачать в хорошем качестве

Diffusion Models: The AI Behind DALL-E & Sora

How do AI systems like DALL-E, Stable Diffusion, and OpenAI's Sora generate photorealistic images from text descriptions? The answer is diffusion models—a breakthrough in generative AI that learns to create data by reversing a noise-adding process. Professor Sarah Ostadabbas from Northeastern University explains how diffusion models work, why they outperform GANs, and how recent advancements have revolutionized image, video, and text generation. What Are Diffusion Models? Unlike VAEs or normalizing flows, diffusion models don't rely on predefined latent variable structures. Instead, they learn high-dimensional latent representations the same size as the original data, leading to improved expressivity and sample diversity. The core idea: gradually add noise to data until it becomes indistinguishable from random noise, then learn to reverse this process step-by-step, reconstructing the original data. Key Breakthrough: Denoising Diffusion Probabilistic Models (DDPMs) One of the most important contributions in this field was Denoising Diffusion Probabilistic Models (DDPMs), introduced at NeurIPS 2020. This paper established the foundation for modern diffusion-based generative AI. As diffusion models continue to evolve, they will reshape industries: • Creative Media: Advertising, film production, graphic design • Gaming: Asset generation, texture creation, procedural content • Medical Imaging: Synthetic data generation, image enhancement, diagnosis support • Robotics: Simulation environments, sensor data synthesis • Scientific Research: Molecular design, materials discovery Future Research Directions: • Making models faster through better sampling techniques • Improving efficiency with distillation and compression • Adapting to real-world applications with domain-specific training • Enabling better control and editability in generation • Combining diffusion with other AI techniques Why Learn This: Diffusion models represent the cutting edge of generative AI. Understanding how they work—from noise prediction to latent space optimization—prepares you to build, fine-tune, and deploy state-of-the-art generative systems. Who Should Learn This: • Computer vision researchers working on generative models • ML engineers building image/video generation systems • AI practitioners interested in latest generative techniques • Data scientists exploring synthetic data generation • Anyone wanting to understand how DALL-E and Stable Diffusion work Course Context: This lesson is part of Northeastern University's Machine Learning with Small Data course, exploring how generative models like diffusion can create synthetic training data to augment small datasets—a critical technique for data-scarce environments. Ready to understand the AI behind DALL-E, Stable Diffusion, and Sora? 🔗 Machine Learning with Small Data Part 1: https://www.coursera.org/learn/machin... 🔗 Machine Learning with Small Data Part 2: https://www.coursera.org/learn/machin... 🔗 Northeastern Online Programs: https://online.northeastern.edu/ Advance your career with industry-driven programs in business, AI, healthcare, and technology—designed for working professionals.