Comparing neural models using their perceptual discriminability predictions

Jing Yang Zhou, Chanwoo Chun, Ajay Subramanian, Eero P. Simoncelli

Research output: Contribution to journalConference articlepeer-review


Internal representations are not uniquely identifiable from perceptual measurements: different representations can generate identical perceptual predictions, and similar representations may predict dissimilar percepts. Here, we generalize a previous method (“Eigendistortions” – Berardino et al., 2017) to enable comparison of models based on their metric tensors, which can be verified perceptually. Metric tensors characterize sensitivity to stimulus perturbations, reflecting both the geometric and stochastic properties of the representation, and providing an explicit prediction of perceptual discriminability. Brute force comparison of model-predicted metric tensors would require estimation of human perceptual thresholds along an infeasibly large set of stimulus directions. To circumvent this “perceptual curse of dimensionality”, we compute and measure discrimination capabilities for a small set of most-informative perturbations, reducing the measurement cost from thousands of hours (a conservative estimate) to a single trial. We show that this single measurement, made for a variety of different test stimuli, is sufficient to differentiate models, select models that better match human perception, or generate new models that combine the advantages of existing models. We demonstrate the power of this method in comparison of (1) two models for trichromatic color representation, with differing internal noise; and (2) two autoencoders trained with different regularizers.

Original languageEnglish (US)
Pages (from-to)315-325
Number of pages11
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event1st Workshop on Unifying Representations in Neural Models, UniReps 2023 - New Orleans, United States
Duration: Dec 15 2023 → …

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


Dive into the research topics of 'Comparing neural models using their perceptual discriminability predictions'. Together they form a unique fingerprint.

Cite this