Learning with Noisy Labels and Data

• Learning Maximum Margin Markov Networks from examples with missing labels
  Vojtech Franc (Center for Machine Perception)*; Andrii Yermakov (Czech Technical University in Prague)
• QActor: Active Learning on Noisy Labels
  Taraneh Younesian (TU Delft)*; Zilong Zhao (Delft University of Technology); Amirmasoud Ghiassi (TU Delft); Robert Birke (ABB Research); Lydia Chen (TU Delft)
• Robust Regression for Monocular Depth Estimation
  Julian Lienen (Paderborn University)*; Nils Nommensen (TIB - Leibniz Information Center for Science and Technology); Ralph Ewerth (TIB - Leibniz Information Center for Science and Technology); Eyke Hüllermeier (University of Munich)
• Improving Deep Label Noise Learning with Dual Active Label Correction
  Shao-Yuan Li (Nanjing University of Aeronautics and Astronautics)*; Ye Shi (Nanjing University of Aeronautics and Astronautics); Sheng-Jun Huang (Nanjing University of Aeronautics and Astronautics); Songcan Chen (Nanjing University of Aeronautics and Astronautics)
• A Two-Stage Training Framework with Feature-Label Matching Mechanism for Learning from Label Proportions
  Haoran Yang (The Chinese University of Hong Kong)*; Wanjing Zhang (Central University of Finance and Economics ); Wai Lam (The Chinese University of Hong Kong)
• ASD-Conv: Monocular 3D object detection network based on Asymmetrical Segmentation Depth-aware Convolution
  Xingyuan Yu (Wuhan University); Neng Du (Wuhan University); Ge Gao (Wuhan University)*; Fan Wen (Wuhan University)