Denoising Diffusion Probabilistic Models.pdf
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Whether you're a researcher, practitioner, or enthusiast eager to delve deeper into the mechanisms underlying diffusion processes, this workshop offers a comprehensive exploration. This workshop will feature a blend of lectures, interactive discussions, and practical exercises, led by beloved GDSC AI/ML team. Whether you're a novice or an experienced practitioner, this workshop promises to deepen your understanding of diffusion models and inspire new avenues for research and application. Join us and unlock the secrets of diffusion dynamics!
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Denoising Diffusion Probabilistic Models.pdf
- 1. Denoising Diffusion Probabilistic Models AI and ML Team Shriram (Head) Samyuktaa Sanjai Balajee Shreyas Sai Sushmithaa P
- 2. Image in Math Terms
- 3. Probability Distribution Some of the Common Distributions: → Uniform → Bernoulli → Binomial → Normal → Poisson → Chi Squared Associated Terms: PMF, PDF, CDF
- 4. Image as a distribution
- 5. Transport Maps Types of Modelling
- 9. What can we learn from Thermodynamics
- 13. Noising and the Forward Process
- 14. Denoising and the Reverse Process
- 15. Connection with Stochastic Gradient Langevin Dynamics
- 16. Parametrization
- 17. Why are Diffusion models better than GANS? ● Diffusion Models offer fine-grained control over the generation process ● GANs may suffer from mode collapse, where the generator produces limited or repetitive samples ● Currently they are still slower than GANs at sampling time due to the use of multiple denoising steps
- 18. Diffusion Tools ● DALL-E 3: Developed by OpenAI ● Stable Diffusion: Developed by Stability AI
- 19. ● Graphic design ● Film and animation ● Music and sound design ● Media and gaming industry Use Cases
- 20. → Flow matching for generative modelling, Lipman et.al → Flow straight and fast: learning to generate and transfer data with rectified flow. Liu et. al Give it a read