Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets

• URL: http://arxiv.org/abs/2503.22856v1
• Date: Fri, 28 Mar 2025 20:18:28 GMT
• Title: Generating Synthetic Oracle Datasets to Analyze Noise Impact: A Study on Building Function Classification Using Tweets
• Authors: Shanshan Bai, Anna Kruspe, Xiaoxiang Zhu,
• Abstract summary: In building function (BFC), tweets are collected using geographics and labeled via external databases.<n>The impact of sentence level feature noise remains underexplored, largely due to the lack of clean benchmark datasets for controlled analysis.<n>In this work, we propose a method for generating a synthetic dataset using LLM, designed to contain only tweets that are both correctly labeled and semantically relevant to their associated buildings.
• Score: 16.88765929875316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tweets provides valuable semantic context for earth observation tasks and serves as a complementary modality to remote sensing imagery. In building function classification (BFC), tweets are often collected using geographic heuristics and labeled via external databases, an inherently weakly supervised process that introduces both label noise and sentence level feature noise (e.g., irrelevant or uninformative tweets). While label noise has been widely studied, the impact of sentence level feature noise remains underexplored, largely due to the lack of clean benchmark datasets for controlled analysis. In this work, we propose a method for generating a synthetic oracle dataset using LLM, designed to contain only tweets that are both correctly labeled and semantically relevant to their associated buildings. This oracle dataset enables systematic investigation of noise impacts that are otherwise difficult to isolate in real-world data. To assess its utility, we compare model performance using Naive Bayes and mBERT classifiers under three configurations: real vs. synthetic training data, and cross-domain generalization. Results show that noise in real tweets significantly degrades the contextual learning capacity of mBERT, reducing its performance to that of a simple keyword-based model. In contrast, the clean synthetic dataset allows mBERT to learn effectively, outperforming Naive Bayes Bayes by a large margin. These findings highlight that addressing feature noise is more critical than model complexity in this task. Our synthetic dataset offers a novel experimental environment for future noise injection studies and is publicly available on GitHub.

Related papers

• NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification [7.464154519547575]
  Existing research on learning with noisy labels predominantly focuses on synthetic noise patterns. We constructed a benchmark dataset to better understand label noise in real-world text classification settings. Our findings reveal that while pre-trained models are resilient to synthetic noise, they struggle against instance-dependent noise.
  arXiv  Detail & Related papers  (2024-07-09T06:18:40Z)
• Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark [65.79402756995084]
  Real Acoustic Fields (RAF) is a new dataset that captures real acoustic room data from multiple modalities. RAF is the first dataset to provide densely captured room acoustic data.
  arXiv  Detail & Related papers  (2024-03-27T17:59:56Z)
• Group Benefits Instances Selection for Data Purification [21.977432359384835]
  Existing methods for combating label noise are typically designed and tested on synthetic datasets. We propose a method named GRIP to alleviate the noisy label problem for both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-23T03:06:19Z)
• Impact of Noisy Supervision in Foundation Model Learning [91.56591923244943]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets.<n>We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Representation Learning for the Automatic Indexing of Sound Effects Libraries [79.68916470119743]
  We show that a task-specific but dataset-independent representation can successfully address data issues such as class imbalance, inconsistent class labels, and insufficient dataset size. Detailed experimental results show the impact of metric learning approaches and different cross-dataset training methods on representational effectiveness.
  arXiv  Detail & Related papers  (2022-08-18T23:46:13Z)
• Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels [44.133307197696446]
  The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. We propose a novel feature embedding-based method for deep learning with label noise, termed LabEl NoiseDilution (LEND)
  arXiv  Detail & Related papers  (2022-06-27T02:45:09Z)
• Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations [54.400167806154535]
  Existing research on learning with noisy labels mainly focuses on synthetic label noise. This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N) We show that real-world noisy labels follow an instance-dependent pattern rather than the classically adopted class-dependent ones.
  arXiv  Detail & Related papers  (2021-10-22T22:42:11Z)
• Training Classifiers that are Universally Robust to All Label Noise Levels [91.13870793906968]
  Deep neural networks are prone to overfitting in the presence of label noise. We propose a distillation-based framework that incorporates a new subcategory of Positive-Unlabeled learning. Our framework generally outperforms at medium to high noise levels.
  arXiv  Detail & Related papers  (2021-05-27T13:49:31Z)
• Generation and Analysis of Feature-Dependent Pseudo Noise for Training Deep Neural Networks [0.0]
  Training Deep neural networks (DNNs) on noisy labeled datasets is a challenging problem, because learning on mislabeled examples deteriorates the performance of the network. We propose an intuitive approach to creating feature-dependent noisy datasets by utilizing the training predictions of DNNs on clean datasets that also retain true label information. We conduct several experiments to establish that Pseudo noisy datasets resemble feature-dependent noisy datasets across different conditions.
  arXiv  Detail & Related papers  (2021-05-22T19:15:26Z)
• Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding [76.89426311082927]
  Existing models are trained on clean data, which causes a textitgap between clean data training and real-world inference. We propose a method from the perspective of domain adaptation, by which both high- and low-quality samples are embedding into similar vector space. Experiments on the widely-used dataset, Snips, and large scale in-house dataset (10 million training examples) demonstrate that this method not only outperforms the baseline models on real-world (noisy) corpus but also enhances the robustness, that is, it produces high-quality results under a noisy environment.
  arXiv  Detail & Related papers  (2021-04-13T17:54:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.