Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
This addresses the problem of improving open set recognition for machine learning systems, though it appears incremental by comparing existing uncertainty and generative methods.
The paper tackles open set recognition by analyzing predictive uncertainty for out-of-distribution detection, finding that uncertainty alone is insufficient but complements extreme value theory methods, with generative models outperforming discriminative ones in posterior-based recognition.
We present an analysis of predictive uncertainty based out-of-distribution detection for different approaches to estimate various models' epistemic uncertainty and contrast it with extreme value theory based open set recognition. While the former alone does not seem to be enough to overcome this challenge, we demonstrate that uncertainty goes hand in hand with the latter method. This seems to be particularly reflected in a generative model approach, where we show that posterior based open set recognition outperforms discriminative models and predictive uncertainty based outlier rejection, raising the question of whether classifiers need to be generative in order to know what they have not seen.