We will train our model using the binary crossentropy loss. This is because the labels are not disjoint. For a given abstract, we may have multiple categories. So, we will divide the prediction task into a series of multiple binary classification problems. This is also why we kept the activation function of the classification layer in our model to sigmoid. Researchers have used other combinations of loss function and activation function as well. For example, in Exploring the Limits of Weakly Supervised Pretraining, Mahajan et al. used the softmax activation function and cross-entropy loss to train their models.