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