Summary of Learning with Noisy Ground Truth: From 2d Classification to 3d Reconstruction, by Yangdi Lu et al.
Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction
by Yangdi Lu, Wenbo He
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the limitations of deep neural networks’ success in data-intensive computer vision applications, which heavily rely on massive and clean data. However, obtaining clean data is often challenging in real-world scenarios. For instance, precise annotations of millions of samples are expensive and time-consuming for image classification and segmentation tasks. Additionally, many 3D static scene reconstruction methods assume a static scene with consistent lighting conditions and persistent object positions, which is frequently violated in reality. To address these issues, learning with noisy ground truth (LNGT) has emerged as an effective learning method. This paper proposes a formal definition to unify the analysis of LNGT in various machine learning tasks, including classification and regression. A novel taxonomy is also introduced based on error decomposition and the fundamental definition of machine learning. The paper provides in-depth analysis on memorization effects and discusses potential future research opportunities from 2D classification to 3D reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep neural networks work well when there’s lots of clean data, but that’s not always what happens in real life. In reality, getting all the data we need is hard and expensive. For example, if we want to teach a computer to classify images or separate objects from each other, it takes a lot of time and money to get all the necessary labels. Similarly, when trying to reconstruct 3D scenes, we often assume that nothing changes in the scene, which isn’t always true. To solve these problems, some researchers have started using “learning with noisy ground truth” (LNGT), which can be very effective. This paper defines what LNGT means and how it works in different situations, like classifying images or predicting things. It also talks about how computers might remember certain things instead of learning new ones, and suggests ways that future research could improve our understanding of machine learning. |
Keywords
» Artificial intelligence » Classification » Image classification » Machine learning » Regression
