# A Topological Filter for Learning with Label Noise

arXiv: https://arxiv.org/abs/2012.04835 · HTML: https://mlsanity.com/paper/2012.04835/

## Key facts

- Cited by 76 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 30 of 76)

- [Learning with Feature-Dependent Label Noise: A Progressive Approach](https://mlsanity.com/paper/2103.07756/) (2021)
- [FINE Samples for Learning with Noisy Labels](https://mlsanity.com/paper/2102.11628/) (2021)
- [Sample Selection with Uncertainty of Losses for Learning with Noisy Labels](https://mlsanity.com/paper/2106.00445/) (2021)
- [Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization](https://mlsanity.com/paper/2102.02414/) (2021)
- [NGC: A Unified Framework for Learning with Open-World Noisy Data](https://mlsanity.com/paper/2108.11035/) (2021)
- [Learning with Neighbor Consistency for Noisy Labels](https://mlsanity.com/paper/2202.02200/) (2022)
- [Trigger Hunting with a Topological Prior for Trojan Detection](https://mlsanity.com/paper/2110.08335/) (2021)
- [Topological Detection of Trojaned Neural Networks](https://mlsanity.com/paper/2106.06469/) (2021)
- [Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics](https://mlsanity.com/paper/2209.10015/) (2022)
- [Cycle Representation Learning for Inductive Relation Prediction](https://mlsanity.com/paper/2110.02510/) (2021)
- [Neighborhood Collective Estimation for Noisy Label Identification and Correction](https://mlsanity.com/paper/2208.03207/) (2022)
- [BatchNorm-based Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2311.15367/) (2023)
- [Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection](https://mlsanity.com/paper/2111.14932/) (2021)
- [Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels](https://mlsanity.com/paper/2305.19518/) (2023)
- [Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy](https://mlsanity.com/paper/2311.01002/) (2023)
- [A Tale Of Two Long Tails](https://mlsanity.com/paper/2107.13098/) (2021)
- [Identifying Hard Noise in Long-Tailed Sample Distribution](https://mlsanity.com/paper/2207.13378/) (2022)
- [Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise](https://mlsanity.com/paper/2112.02960/) (2021)
- [Late Stopping: Avoiding Confidently Learning from Mislabeled Examples](https://mlsanity.com/paper/2308.13862/) (2023)
- [CLIPCleaner: Cleaning Noisy Labels with CLIP](https://mlsanity.com/paper/2408.10012/) (2024)
- [Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels](https://mlsanity.com/paper/2203.07788/) (2022)
- [Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning](https://mlsanity.com/paper/2301.06013/) (2023)
- [SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise](https://mlsanity.com/paper/2111.11288/) (2021)
- [Resurrecting Label Propagation for Graphs with Heterophily and Label Noise](https://mlsanity.com/paper/2310.16560/) (2023)
- [AFreeCA: Annotation-Free Counting for All](https://mlsanity.com/paper/2403.04943/) (2024)
- [ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance](https://mlsanity.com/paper/2312.08852/) (2023)
- [BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification](https://mlsanity.com/paper/2203.01937/) (2022)
- [Sample Prior Guided Robust Model Learning to Suppress Noisy Labels](https://mlsanity.com/paper/2112.01197/) (2021)
- [SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training](https://mlsanity.com/paper/2210.04525/) (2022)
- [Noisy Label Processing for Classification: A Survey](https://mlsanity.com/paper/2404.04159/) (2024)

## Abstract (arXiv)

Noisy labels can impair the performance of deep neural networks. To tackle this problem, in this paper, we propose a new method for filtering label noise. Unlike most existing methods relying on the posterior probability of a noisy classifier, we focus on the much richer spatial behavior of data in the latent representational space. By leveraging the high-order topological information of data, we are able to collect most of the clean data and train a high-quality model. Theoretically we prove that this topological approach is guaranteed to collect the clean data with high probability. Empirical results show that our method outperforms the state-of-the-arts and is robust to a broad spectrum of noise types and levels.