📚 https://arxiv.org/abs/2006.11239
🏆 Published in NeurIPS 2020

📄 Denoising Diffusion Probabilistic Models – Day 5

✨ Key Contributions

• Positioned diffusion models as a strong alternative to GANs and VAEs.
• Highlighted training stability with a simple objective.
• Connected diffusion to score matching and Langevin dynamics, providing theoretical grounding.

🎯 Problem Definition

• Assess whether diffusion can serve as a general-purpose generative framework beyond images.
• Identify core challenges like efficiency and computational cost.

🧠 Method / Architecture

• Summarized prior experimental results and theoretical analysis.
• Framed diffusion as “add noise → learn to remove noise”, emphasizing simplicity and power.

🧪 Experiments & Results

• Built upon Day 4 results: competitive or superior to GANs in image generation.
• Showed stable and diverse samples without mode collapse.
• Validated the framework’s robustness across datasets.

🚫 Limitations

• Slow sampling (hundreds to thousands of denoising steps).
• High compute requirements compared to GANs.

🔭 Future Ideas

• Develop faster samplers (e.g., DDIM, latent diffusion).
• Extend to new domains (audio, video, text, multimodal).
• Explore hybrid models combining diffusion with other generative paradigms.

🔁 Personal Reflections

• The final discussion confirms diffusion models are not just a niche, but a foundational paradigm in modern generative AI.
• Despite efficiency limits, their stability and versatility make them central to recent breakthroughs like Stable Diffusion, Imagen, and DALL·E 2.