Classification and comparison via neural networks

İlkay Yıldız, Peng Tian, Jennifer Dy, Deniz Erdoğmuş, James Brown, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


We consider learning from comparison labels generated as follows: given two samples in a dataset, a labeler produces a label indicating their relative order. Such comparison labels scale quadratically with the dataset size; most importantly, in practice, they often exhibit lower variance compared to class labels. We propose a new neural network architecture based on siamese networks to incorporate both class and comparison labels in the same training pipeline, using Bradley–Terry and Thurstone loss functions. Our architecture leads to a significant improvement in predicting both class and comparison labels, increasing classification AUC by as much as 35% and comparison AUC by as much as 6% on several real-life datasets. We further show that, by incorporating comparisons, training from few samples becomes possible: a deep neural network of 5.9 million parameters trained on 80 images attains a 0.92 AUC when incorporating comparisons.

Original languageEnglish (US)
Pages (from-to)65-80
Number of pages16
JournalNeural Networks
StatePublished - Oct 2019


  • Classification
  • Comparison
  • Joint learning
  • Neural network
  • Siamese network

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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