Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction

TL;DR

This paper surveys learning with noisy ground truth across 2D classification and 3D reconstruction, proposing a formal framework, taxonomy, and discussing future research directions in handling noisy data.

Contribution

It introduces a unified formal definition of LNGT, a taxonomy for existing methods, and analyzes the memorization effect and future research opportunities.

Findings

Unified formal definition of LNGT for classification and regression.

Taxonomy classifying existing LNGT methods based on error decomposition.

Discussion on memorization effects and future research directions.

Abstract

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.