46 lines
2.2 KiB
Markdown
46 lines
2.2 KiB
Markdown
---
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title: Research Approach
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allDay: true
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date: 2024-10-10
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completed:
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type: single
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endDate: 2024-10-22
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---
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5 Pages
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**TARGET: 2800-3000 words**
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# Outline
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1. What are unperturbed perturbations and why are they important?
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2. What is a generative diffusion model?
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3. Where have generative diffusion models been used?
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4. How will generative diffusion models generate new plants?
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5. How can we know if these new plants are in the set of 'controllable' plants?
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6. How are we going to get training data?
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Something to justify, why diffusion model as opposed to other generative AI
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# First Draft
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## Story
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1. Generating unstructured perturbations is pretty hard
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2. They need to be 'random'
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3. diffusion model is good at creating novel examples (cite!)
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4. we can use diffusion models to solve this problem of generating examples
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5. But how does a diffusion model work?
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6. Diffusion model uses two processes
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7. a forward process that introduces noise into an 'input'
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8. this forward process does this using several small steps of noise
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9. These are called timesteps
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10. This noise is a gaussian noise
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11. over time this forward process degrades the input until it is unrecognizable
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12. This takes several several iterations, but is dependent on the amount of noise in each step
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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
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14. A reverse process that tries to remove the noise
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15. But if we destroy the input how can we do this?
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16. Well we train a neural network as a denoiser.
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17. Because the diffusion model forward steps are small and gaussian, we can know the reverse step is also a gaussian distribution.
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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.
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19. What does this mean for
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## Writin some stuff
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The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models. Diffusion models |