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4 Qualifying Exam/2 Writing/3. QE Research Approach.md
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@ -29,10 +29,13 @@ Something to justify, why diffusion model as opposed to other generative AI
6. Diffusion model uses two processes
7. a forward process that introduces noise into an 'input'
8. this forward process does this using several small steps of noise
9. This noise is a gaussian noise
10. over time this forward process degrades the input until it is unrecognizable
11. This takes several several iterations, but is dependent on the amount of noise in each step
12. The markov chain that this creates is also gaussian at every step, until the noise at the end of the day is some gaussian distribution. Usually mean 0 std. dev beta
13. A reverse process that tries to remove the noise
14. But if we destroy the input how can we do this?
15. Well we train a neural network as a denoiser.
9. These are called timesteps
10. This noise is a gaussian noise
11. over time this forward process degrades the input until it is unrecognizable
12. This takes several several iterations, but is dependent on the amount of noise in each step
13. The markov chain that this creates is also gaussian at every step, until the noise at the end of the day is some gaussian distribution. Usually mean 0 std. dev beta
14. A reverse process that tries to remove the noise
15. But if we destroy the input how can we do this?
16. Well we train a neural network as a denoiser.
17. Because the diffusion model forward steps are small and gaussian, we can know the reverse step is also a gaussian distribution.
18. So for our neural network, what we're trying to learn is the mean and standard deviation of the reverse steps for a given timestep.