The neural networks were developed using PyTorch 1.3.1 together with the PyTorch wrapper PyTorch Lightning 1.0.8 . The experiments were conducted using Nvidia K80 GPUs with driver version 450.80.02, CUDA 11.0. The simple neural network had one hidden layer with 10 neurons; while the complex neural network had three hidden layers with 100, 50 and 25 neurons, respectively. The output layers consisted of two neurons (one for each class) and the input layer corresponded to the one-hot encoding. The complex neural network used dropout with probability 0.5 while the simple neural network used no dropout. Moreover, the networks used Leaky ReLU as activation functions for the hidden layers and softmax as the activation function of the output. Optimization of the parameters was performed using AdamW with a learning rate of 0.001, β1 = 0.9, β2 = 0.99, ε e = 10 -8 , a weight decay of 0.01 and a mini-batch size of 8. The networks were trained for 50 epochs after each query of active learning. For each set of a specific number of initially labelled data points, the same initial weights of the initial epoch were utilized.