Ground Truth
"Ground truth" refers to the accurate, verifiable data used to train and evaluate machine learning models. Current research focuses on addressing challenges arising from incomplete, noisy, or changing ground truth data, employing techniques like robust loss functions, self-supervised learning, and data augmentation to improve model accuracy and reliability. These advancements are crucial for various applications, including medical image analysis, autonomous driving, and remote sensing, where obtaining perfect ground truth is often impractical or impossible, impacting the development of robust and reliable AI systems. The development of novel methods for handling imperfect ground truth is a significant area of ongoing research, driving improvements in model performance and generalization across diverse domains.
Papers
Looking for Tiny Defects via Forward-Backward Feature Transfer
Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti, Luigi Di Stefano
Robust Learning under Hybrid Noise
Yang Wei, Shuo Chen, Shanshan Ye, Bo Han, Chen Gong
Addressing Relative Pose Impact on UWB Localization: Dataset Introduction and Analysis
Jun Hyeok Choe, Inwook Shim
XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis
Hao Li, Ming Yuan, Yan Zhang, Chenming Wu, Chen Zhao, Chunyu Song, Haocheng Feng, Errui Ding, Dingwen Zhang, Jingdong Wang
Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools
Francisca Silva-Clavería, Carmen Serrano, Iván Matas, Amalia Serrano, Tomás Toledo-Pastrana, David Moreno-Ramírez, Begoña Acha
Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time
Salvatore Greco, Bartolomeo Vacchetti, Daniele Apiletti, Tania Cerquitelli
Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification
Zhihui Tian, John Upchurch, G. Austin Simon, José Dubeux, Alina Zare, Chang Zhao, Joel B. Harley