Efficient Pairwise Difference Learning: A Comparative Study of Encoding Strategies and Training Pair Selection

arXiv:2610.21644v1 Announce Type: cross Abstract: Pairwise Difference Learning for Classification (PDC) enhances supervised learning in low-data regimes by training a binary similarity classifier on pairs of instances rather than individual samples, effectively inflating the training set from $n$ to $O(n^2)$ examples. However, the PDC framework leaves critical implementation choices underspecified, particularly regarding input representation and pair selection strategies. This work systematically investigates these design decisions through extensive empirical evaluation. We compare four featur

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