Intriguing properties of generative classifiers

arXiv:2309.16779v2 Announce Type: replace-cross
Abstract: What is the best paradigm to recognize objects -- discriminative inference (fast but potentially prone to shortcut learning) or using a generative model (slow but potentially more robust)? We build on recent advances in generative modeling that turn text-to-image models into classifiers. This allows us to study their behavior and to compare them against discriminative models and human psychophysical data. We report four intriguing emergent properties of generative classifiers: they show a record-breaking human-like shape …

abstract advances arxiv behavior build classifiers cs.ai cs.cv cs.lg generative generative modeling image inference modeling objects paradigm q-bio.nc robust shortcut stat.ml study text text-to-image type