2022
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
ECCV 2022poster
"Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy labels on the fly. However, there is no safeguard on the lab…