
Diffusion Models and Their Applications
Interactive Video
•
Computers
•
11th Grade - University
•
Hard
Thomas White
FREE Resource
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary goal of diffusion models in image generation?
To add noise to images
To create static images
To enhance image resolution
To reverse noise and generate coherent images
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How do diffusion models compare to GANs in terms of perceptual quality?
They are about the same
Diffusion models are generally better
Diffusion models are generally worse
GANs are significantly better
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of Gaussian noise in the forward diffusion process?
To change image colors
To reduce image size
To gradually corrupt the image
To enhance image details
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is a small step size beneficial in the reverse process of diffusion models?
It complicates the reverse process
It decreases model accuracy
It makes learning to undo steps easier
It increases computational cost
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the training objective of diffusion models similar to?
Recurrent Neural Networks
Variational Autoencoders
Autoencoders
Generative Adversarial Networks
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the Kullback-Leibler divergence term in the training objective encourage?
Minimizing data density
Increasing model complexity
Maximizing data density
Aligning the approximate posterior with the prior
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is one method to guide the diffusion process conditionally?
Using a fixed noise level
Using a separate classifier
Ignoring the conditioning variable
Randomly selecting samples
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