What are Diffusion Models?
📰 Lilian Weng's Blog
Diffusion models are a new type of generative models that can learn complex data distributions and generate high-quality images
Action Steps
- Learn the basics of generative models
- Understand the concept of diffusion models
- Explore the applications of diffusion models in image generation
- Read the recommended blog post on score-based generative modeling for further insight
Who Needs to Know This
AI engineers and researchers can benefit from understanding diffusion models to improve generative modeling capabilities, while product managers can explore their applications in computer vision tasks
Key Insight
💡 Diffusion models can learn any arbitrarily complex data distribution and are tractable for analytical evaluation
Share This
📸 Diffusion models can generate high-quality images! Competitive to SOTA GAN
Key Takeaways
Diffusion models are a new type of generative models that can learn complex data distributions and generate high-quality images
Full Article
<!-- Diffusion models are a new type of generative models that are flexible enough to learn any arbitrarily complex data distribution while tractable to analytically evaluate the distribution. It has been shown recently that diffusion models can generate high-quality images and the performance is competitive to SOTA GAN. --> <p><span class="update">[Updated on 2021-09-19: Highly recommend this blog post on <a href="https://yang-song.github.io/blog/2021/score/">score-based generative modeling</a>
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