Deep neural networks has been highly successful in data-intense computer
vision applications, while such success relies heavily on the massive and clean
data. In real-world scenarios, clean data sometimes is difficult to obtain. For
example, in image classification and segmentation tasks, precise annotations of
millions samples are generally very expensive and time-consuming. In 3D static
scene reconstruction task, most NeRF related methods require the foundational
assumption of the static scene (e.g. consistent lighting condition and
persistent object positions), which is often violated in real-world scenarios.
To address these problem, learning with noisy ground truth (LNGT) has emerged
as an effective learning method and shows great potential. In this short
survey, we propose a formal definition unify the analysis of LNGT LNGT in the
context of different machine learning tasks (classification and regression).
Based on this definition, we propose a novel taxonomy to classify the existing
work according to the error decomposition with the fundamental definition of
machine learning. Further, we provide in-depth analysis on memorization effect
and insightful discussion about potential future research opportunities from 2D
classification to 3D reconstruction, in the hope of providing guidance to
follow-up research.

通过对 LNGT 的分析，我们提出了一种新的分类方法，并进行了有关记忆效应和未来研究机会的探讨，为后续研究提供指导。