Representing text as abstract images enables image classifiers to also simultaneously classify text
TABLE II: Hyperparameters that differ between the two network architectures. See [11] for more details on the network architecture.
['Hyperparameter', 'AlexNet', 'This work', 'Rationale for modification']
AlexNet was originally designed to classify colour images (224×224×3-pixel bitmaps) amongst 1,000 classes. We use the default settings for the DIGITS solver (stochastic gradient descent), batch size (100), and number of training epochs (30). Rather than use the default learning rate (0.01), we use a sigmoid decay function to progressively decrease the learning rate from 0.01 to 0.001 over the course of the 30 training epochs, as testing indicated that this produced slightly higher accuracies. Instead of the 1,000-neuron softmax output layer in AlexNet, we use a 2-neuron softmax output layer, which outputs a probability distribution across our two possible classes (match/non-match).