Running the code is simple, simply perform
python run.py active|expert|gan train|evaluate dataset
Where dataset is some folder located in ./datasets
Currently in refactor
-
Expert Classifier
- learning rate scheduling: current model converges after 2-3 epochs to around 73%
- class balancing?
- data augmentation? (probably through lighting changes?)
-
GAN
- class balancing?
- larger kernel size in earlier convolutional layers
- better regularization
- increase output resolution??
- potentially do more epochs
-
Need to make some damn plots
- GAN sample
- expert classifier confusion matrix