Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction
<|reference_start|>Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction: 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.<|reference_end|>
@article{lu2024learning,
    title={Learning with Noisy Ground Truth: From 2D Classification to 3D
  Reconstruction},
    author={Yangdi Lu, Wenbo He},
    journal={arXiv preprint arXiv:2406.15982},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15982},
    primaryClass={cs.CV cs.AI cs.LG}
}