At the moment, the training set, validation set and test set are fixed, and new annotated patches are added only to the training set after each active-learning iteration, leaving the validation set unchanged.
Using K-fold cross validation would allow to make use of all the samples to train the network, especially when few data samples are available.
One potential issue coming with the K-fold cross validation is the extension of the training time.
At the moment, the training set, validation set and test set are fixed, and new annotated patches are added only to the training set after each active-learning iteration, leaving the validation set unchanged.
Using K-fold cross validation would allow to make use of all the samples to train the network, especially when few data samples are available.
One potential issue coming with the K-fold cross validation is the extension of the training time.