---
title: Research Approach
allDay: true
date: 2024-10-10
completed: 
type: single
endDate: 2024-10-22
---

5 Pages
**TARGET: 2800-3000 words**
# Outline
1. What are unperturbed perturbations and why are they important?
2. What is a generative diffusion model?
3. Where have generative diffusion models been used?
4. How will generative diffusion models generate new plants?
5.  How can we know if these new plants are in the set of 'controllable' plants?
6. How are we going to get training data?

Something to justify, why diffusion model as opposed to other generative AI

# First Draft
## Story
1. Generating unstructured perturbations is pretty hard
2. They need to be 'random'
3. diffusion model is good at creating novel examples (cite!)
4. we can use diffusion models to solve this problem of generating examples
5. But how does a diffusion model work?
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.