Experiments on different dataset on how to grow networks during training to learn new image categories.
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CIFAR-10 Multiruns + Data Analysis.ipynb
CIFAR-100-Growing.ipynb
Growing-CIFAR-10.ipynb
GrowingMNIST.ipynb
LICENSE
Masterthesis_Final.pdf
README.md
TestAccuracies.csv
Unbalanced Data Sets & Catastrophic Forgetting.ipynb

README.md

Progressively Growing Neural Networks

This is code to several experiments on different dataset (MNIST, CIFAR-10 and CIFAR-100) on how to grow networks during training and to learn new image categories. It is the code underlying my master thesis which is also provided here for a better understanding and some additional context. The file TestAccuracies.csv contains all my results and is used in the analysis parts of the code. You can of course replicate this data on your own with the code provided but since this will take several days I provide my results already.

Dependencies

  • jupyter notebook
  • tensorflow
  • numpy
  • pandas
  • pickle

For plotting and data analysis:

  • matplotlib
  • seaborn
  • augmentation module from https://github.com/aleju/imgaug for CIFAR-100 experiments
  • ptitprince for some of the plots
  • statsmodels & scipy for significance tests and linear regression