Multi-annotator Deep Learning: A Probabilistic Framework for Classification

5 April 2023

Papers citing "Multi-annotator Deep Learning: A Probabilistic Framework for Classification"

8 / 8 papers shown

Title
crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels M. Herde Lukas Lührs Denis Huseljic Bernhard Sick 22 0 0 12 Apr 2025
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans M. Herde Denis Huseljic Lukas Rauch Bernhard Sick 19 1 0 30 Jul 2024
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension M. Herde Lukas Lührs Denis Huseljic Bernhard Sick 21 3 0 06 May 2024
Learning to Complement with Multiple Humans Zheng Zhang Cuong C. Nguyen Kevin Wells Thanh-Toan Do Gustavo Carneiro 16 0 0 22 Nov 2023
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification M. Herde Denis Huseljic Bernhard Sick A. Calma 27 19 0 23 Sep 2021
Simple Modifications to Improve Tabular Neural Networks J. Fiedler LMTD 75 19 0 06 Aug 2021
Deep Cosine Metric Learning for Person Re-Identification N. Wojke Alex Bewley 31 349 0 02 Dec 2018
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 279 39,083 0 01 Sep 2014