Probabilistic Self-supervised Learning via Scoring Rules Minimization

no code implementations • 5 Sep 2023 • Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei

In this paper, we propose a novel probabilistic self-supervised learning via Scoring Rule Minimization (ProSMIN), which leverages the power of probabilistic models to enhance representation quality and mitigate collapsing representations.

Knowledge Distillation Out-of-Distribution Detection +2

Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning

no code implementations • 28 Aug 2023 • Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei

Ensembling a neural network is a widely recognized approach to enhance model performance, estimate uncertainty, and improve robustness in deep supervised learning.

Ensemble Learning Out-of-Distribution Detection +1