Data mining -- 2

Topic 2: Image Generation with Probabilistic Diffusion Models
Introduction:
1. DDPM - Diffusion Models Beat GANs on Image Synthesis (Machine Learning
Research Paper Explained)
2. What are Diffusion Models?
Paper:
[1]Denoising Diffusion Probabilistic Models (DDPM)
Reference code: code1 or code2 or code3
Problem 1: What is the problem that the paper aims to solve, and why is this problem
important or interesting? Answer this question in two sentences.
Problem 2: Implement DDPM and test it on 2-dimensional Swiss roll data. You should
output Fig.3 of the blog intro 2.
Problem 3: Test DDPM on the mixture of 8 Gaussians and 25 Gaussians respectively.
These Gaussians can be either identical or with different parameters at your choice. You
should output figures like those in Problem 2.
Problem 4: Test DDPM on MNIST dataset. Visualize the denoising process (You should
output a figure like Fig. 6 in the paper).
Problem 5: Perform an interpolation experiment with your trained model from Problem 4
(You should output a figure like Fig. 8 in the paper).