# Joint Negative and Positive Learning for Noisy Labels

arXiv: https://arxiv.org/abs/2104.06574 · HTML: https://mlsanity.com/paper/2104.06574/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 35 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [Unsupervised Label Noise Modeling and Loss Correction](https://mlsanity.com/paper/1904.11238/) (2019)
- [Training Deep Neural Networks on Noisy Labels with Bootstrapping](https://mlsanity.com/paper/1412.6596/) (2014)
- [Joint Optimization Framework for Learning with Noisy Labels](https://mlsanity.com/paper/1803.11364/) (2018)
- [Combating noisy labels by agreement: A joint training method with co-regularization](https://mlsanity.com/paper/2003.02752/) (2020)
- [Normalized Loss Functions for Deep Learning with Noisy Labels](https://mlsanity.com/paper/2006.13554/) (2020)
- [Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise](https://mlsanity.com/paper/1802.05300/) (2018)
- [Probabilistic End-to-end Noise Correction for Learning with Noisy Labels](https://mlsanity.com/paper/1903.07788/) (2019)
- [CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise](https://mlsanity.com/paper/1711.07131/) (2017)
- [Dimensionality-Driven Learning with Noisy Labels](https://mlsanity.com/paper/1806.02612/) (2018)
- [Learning from Noisy Labels with Distillation](https://mlsanity.com/paper/1703.02391/) (2017)
- [Learning to Learn from Noisy Labeled Data](https://mlsanity.com/paper/1812.05214/) (2018)
- [NLNL: Negative Learning for Noisy Labels](https://mlsanity.com/paper/1908.07387/) (2019)
- [Learning From Noisy Large-Scale Datasets With Minimal Supervision](https://mlsanity.com/paper/1701.01619/) (2017)
- [Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks](https://mlsanity.com/paper/1706.00038/) (2017)
- [Making Risk Minimization Tolerant to Label Noise](https://mlsanity.com/paper/1403.3610/) (2014)
- [Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels](https://mlsanity.com/paper/1705.01936/) (2017)
- [Improving Generalization by Controlling Label-Noise Information in Neural Network Weights](https://mlsanity.com/paper/2002.07933/) (2020)
- [Learning Deep Networks from Noisy Labels with Dropout Regularization](https://mlsanity.com/paper/1705.03419/) (2017)
- [A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels](https://mlsanity.com/paper/1802.02679/) (2018)

## Built on by (most cited first, 30 of 34)

- [Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels](https://mlsanity.com/paper/2203.03884/) (2022)
- [Efficient Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2403.18293/) (2024)
- [UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning](https://mlsanity.com/paper/2203.14542/) (2022)
- [Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2303.03770/) (2023)
- [Continual Test-time Domain Adaptation via Dynamic Sample Selection](https://mlsanity.com/paper/2310.03335/) (2023)
- [Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing](https://mlsanity.com/paper/2204.11573/) (2022)
- [Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning](https://mlsanity.com/paper/2312.09505/) (2023)
- [Rethinking Weak Supervision in Helping Contrastive Learning](https://mlsanity.com/paper/2306.04160/) (2023)
- [Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels](https://mlsanity.com/paper/2207.14476/) (2022)
- [Translation Consistent Semi-supervised Segmentation for 3D Medical Images](https://mlsanity.com/paper/2203.14523/) (2022)
- [CrossSplit: Mitigating Label Noise Memorization through Data Splitting](https://mlsanity.com/paper/2212.01674/) (2022)
- [L2B: Learning to Bootstrap Robust Models for Combating Label Noise](https://mlsanity.com/paper/2202.04291/) (2022)
- [Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise](https://mlsanity.com/paper/2308.06861/) (2023)
- [Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation](https://mlsanity.com/paper/2306.02314/) (2023)
- [Dirichlet-Based Prediction Calibration for Learning with Noisy Labels](https://mlsanity.com/paper/2401.07062/) (2024)
- [Noisy Label Processing for Classification: A Survey](https://mlsanity.com/paper/2404.04159/) (2024)
- [PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels](https://mlsanity.com/paper/2201.10836/) (2022)
- [Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection](https://mlsanity.com/paper/2402.11242/) (2024)
- [Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning](https://mlsanity.com/paper/2206.11011/) (2022)
- [Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations](https://mlsanity.com/paper/2401.04390/) (2024)
- [Tripartite: Tackle Noisy Labels by a More Precise Partition](https://mlsanity.com/paper/2202.09579/) (2022)
- [Active Negative Loss: A Robust Framework for Learning with Noisy Labels](https://mlsanity.com/paper/2412.02373/) (2024)
- [Combating Noisy Labels in Long-Tailed Image Classification](https://mlsanity.com/paper/2209.00273/) (2022)
- [Fully Test-Time Adaptation for Monocular 3D Object Detection](https://mlsanity.com/paper/2405.19682/) (2024)
- [Can LLMs Learn to Reason Robustly under Noisy Supervision?](https://mlsanity.com/paper/2604.03993/) (2026)
- [Learning from Data with Noisy Labels Using Temporal Self-Ensemble](https://mlsanity.com/paper/2207.10354/) (2022)
- [Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation](https://mlsanity.com/paper/2505.18787/) (2025)
- [Unlearning the Noisy Correspondence Makes CLIP More Robust](https://mlsanity.com/paper/2507.03434/) (2025)
- [On Robust Learning from Noisy Labels: A Permutation Layer Approach](https://mlsanity.com/paper/2211.15890/) (2022)
- [Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation](https://mlsanity.com/paper/2404.10574/) (2024)

## Abstract (arXiv)

Training of Convolutional Neural Networks (CNNs) with data with noisy labels is known to be a challenge. Based on the fact that directly providing the label to the data (Positive Learning; PL) has a risk of allowing CNNs to memorize the contaminated labels for the case of noisy data, the indirect learning approach that uses complementary labels (Negative Learning for Noisy Labels; NLNL) has proven to be highly effective in preventing overfitting to noisy data as it reduces the risk of providing faulty target. NLNL further employs a three-stage pipeline to improve convergence. As a result, filtering noisy data through the NLNL pipeline is cumbersome, increasing the training cost. In this study, we propose a novel improvement of NLNL, named Joint Negative and Positive Learning (JNPL), that unifies the filtering pipeline into a single stage. JNPL trains CNN via two losses, NL+ and PL+, which are improved upon NL and PL loss functions, respectively. We analyze the fundamental issue of NL loss function and develop new NL+ loss function producing gradient that enhances the convergence of noisy data. Furthermore, PL+ loss function is designed to enable faster convergence to expected-to-be-clean data. We show that the NL+ and PL+ train CNN simultaneously, significantly simplifying the pipeline, allowing greater ease of practical use compared to NLNL. With a simple semi-supervised training technique, our method achieves state-of-the-art accuracy for noisy data classification based on the superior filtering ability.