Graph contrastivelearning (GCL), by training GNNs to maximize the correspondence between the representations of the same graph in its different augmented forms, may yield robust and transferable GNNs even without using labels.

Existing FER methods typically report overall accuracy on highly imbalanced test sets but exhibit low performance in terms of the mean accuracy across all expression classes.

However, these models suffer from quadratic computational cost in the input sequence lengthn to compute pairwise attention in each layer.

Let defined denote conditional (x)= P(y =1|x).

However, statements different from the original statement could also be logically consistent with respecttoagivencontext.