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Autoencoders implementation for the 8th sheet of the course of Machine Learning and Physics at Heidelberg University

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Autoencoders

What about it

In this small project (bonus exercise sheet for the course of Machine Learning and Physics) the objective was to build an autoencoder using various two different sub-architectures, the first one being MLPs and the second one being CNNs. As a dataset we got a set of jet pictures from professot Tilman Plehn and we had to come up with a series of observations on how the two architectures that we came up with were able to reconstruct the images that were given as inputs.

Work in progress

I still want to put some effort in this project, even though the due date is already over, because I want to understand a couple of things that didn't really sit right with me, here is an approximative roadmap of the process:

  • Clean the code and make it easier to understand
  • Understand why the CNN has lower accuracy than the MLP substructure I found a way to make sure that the CNN has higher accuracy than the MLP substructure but it takes 5 times to run and the ROC curve is kind of a weird result.
  • Make the Convolutional Neural Network more efficient.

Quality of the solution

Even though our solution was not totally complete it was deemed close to perfect, thus it's possible for anyone to see it as a tutorial that is very close to beginner level (bragging, but still down to earth).