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MPhil in Data Intensive Science
Submission: 11:59pm on Thursday the 28th of March
Coursework v1.0
Major Module M2: Application of Machine Learning
The coursework will be submitted via a GitLab repository which we will
create for you. You should place all your code and your report in this
repository. You should write a report with up to 3000 words that
describes your work. Your report should contain figures and tables to
support your analysis and discussion. The report should be in PDF
format in a folder called "report". You will be provided access to the
repository until the stated deadline. After this you will lose access which
will constitute submission of your work.
The code associated with the coursework should be written in Python and
follow best software development practice as defined by the Research
Computing module. This should include:
• Writing clear readable code that is compliant with a common style
guide and uses suitable build management tools.
• Providing appropriate documentation that is compatible with
auto-documentation tools.
• The project must be well structured (sensible folder structure,
README.md, licence etc..) following standard best practice.
• Uses appropriate version control best practice, including branching
for development and testing, and commit hooks to protect ‘main‘.
• Appropriate containerisation to ensure portability of the project to
other computers and operating systems.
Note that extra marks will *not* be awarded for extremely high-performance
models trained on large compute resources, so don’t worry about this
aspect of things (though it is fine to seek the absolute best possible
performance purely for your own personal satisfaction). The main thing
considered in the grading of this report is the quality of your report and
analysis, and demonstrated understanding of the course material and the
problem. In other words, you can do this coursework entirely on a laptop.
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2
Introduction For this coursework, you will be tasked with building and training
different types of diffusion models on MNIST in PyTorch. As part of this, you will be
asked to design a custom type of diffusion model using a degradation strategy of your
choice. The choice of degradation is quite open-ended; you should be creative in your
choice.
Background Your primary resource should be to consult Chapter 18 of the Prince
book for a detailed introduction to diffusion models. As a secondary, optional resource,
you may find the paper demonstrating denoising diffusion probabilistic models for image
generation by Ho et al., (2020) helpful in understanding the motivation of this class of
model: coursework/ho_2020.pdf. Finally, I also include a blog post by Lilian Weng
which describes diffusion models at a higher level: coursework/weng_2021.pdf.
Note: I break the following two questions into three parts each, a, b, and c. However, this
is purely for the sake of breaking up the question itself and does not imply that the
subparts are of equal difficulty or should be equivalent length.
1 Training a Diffusion Model [40]
(a) First, you will train a regular denoising diffusion probabilistic model on
MNIST as a warm up. In the coursework folder you will find the notebook
coursework/coursework_start.ipynb which contains a working
implementation of a diffusion model and training loop, on the MNIST dataset.
Briefly describe this model and the training algorithm in your report.
(b) Document the process of training this model on the provided dataset,
visualizing standard metrics such as the loss curve and the quality of the samples
generated.

When I trained it with the default hyperparameters, I found it needed about 24
epochs to start consistently generating symbols (though those symbols didn’t look
like numbers at all yet), and maybe 50 epochs until I started seeing numbers get
generated.

Do this for two different sets of hyperparameters, and discuss the differences in the
results.
(c) Present an analysis of each trained model. In this analysis, you should present
both high quality and low quality samples from the trained model.
2 Custom Degradation [60]
(a) In the "Cold Diffusion" paper by Bansal et al., (2022), which can be found at
coursework/bansal_2022.pdf, it was shown that a wide variety of image
degradations can be used to train diffusion models, rather than only Gaussian noise.
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3
Design a custom degradation strategy for images that you can use to train a
diffusion model. You may use the Bansal et al. paper for guidance on this, but you
should try to be creative in your choice of degradation, rather than simply using
Gaussian noise or a close variant. Describe your degradation strategy in your
report.
(b) Modify the code from the first part of this coursework to train and sample a
diffusion model using your custom degradation strategy. (You may also modify the
model architecture if desired.) Again, you should train this on the same MNIST
dataset. As before, document the process of training this model, and present a
detailed analysis of the results.
(c) After this, present a comparison of the two degradation strategies (i.e., the
standard Gaussian noise degradation and your custom degradation strategy).
Evaluate the fidelity of the samples generated by the two models, discussing any
differences between them.
END OF PAPER
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