Dropout Injection at Test Time for Post Hoc Uncertainty Quantification
in Neural Networks

Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks

6 February 2023

Fabio Roli

Papers citing "Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks"

7 / 7 papers shown

Title
Ranking pre-trained segmentation models for zero-shot transferability Joshua Talks Anna Kreshuk 130 0 0 01 Mar 2025
Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection Abrar Anwar Rohan Gupta Zain Merchant Sayan Ghosh Willie Neiswanger Jesse Thomason OffRL 67 1 0 14 Feb 2025
PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis Chuanhao Sun Thanos Triantafyllou Anthos Makris Maja Drmač Kai Xu Luo Mai Mahesh Marina 28 0 0 07 Oct 2024
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection Junfei Yi Jianxu Mao Tengfei Liu Mingjie Li Hanyu Gu Hui Zhang Xiaojun Chang Yaonan Wang 34 2 0 11 Jun 2024
Epistemic Uncertainty Quantification For Pre-trained Neural Network Hanjing Wang Qiang Ji UQCV 39 2 0 15 Apr 2024
Adversarial Attacks Against Uncertainty Quantification Emanuele Ledda Daniele Angioni Giorgio Piras Giorgio Fumera Battista Biggio Fabio Roli AAML 32 2 0 19 Sep 2023
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015