# Noisy Concurrent Training for Efficient Learning under Label Noise

arXiv: https://arxiv.org/abs/2009.08325 · HTML: https://mlsanity.com/paper/2009.08325/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning](https://mlsanity.com/paper/2203.14542/) (2022)
- [CrossSplit: Mitigating Label Noise Memorization through Data Splitting](https://mlsanity.com/paper/2212.01674/) (2022)
- [Sample Prior Guided Robust Model Learning to Suppress Noisy Labels](https://mlsanity.com/paper/2112.01197/) (2021)
- [Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels](https://mlsanity.com/paper/2210.08826/) (2022)
- [Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations](https://mlsanity.com/paper/2401.04390/) (2024)
- [Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training](https://mlsanity.com/paper/2107.05736/) (2021)
- [Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition](https://mlsanity.com/paper/2208.10221/) (2022)
- [Robust Noisy Label Learning via Two-Stream Sample Distillation](https://mlsanity.com/paper/2404.10499/) (2024)
- [Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations](https://mlsanity.com/paper/2107.04746/) (2021)
- [Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition](https://mlsanity.com/paper/2305.01884/) (2023)
- [ASM: Adaptive Sample Mining for In-The-Wild Facial Expression Recognition](https://mlsanity.com/paper/2310.05618/) (2023)
- [COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement](https://mlsanity.com/paper/2506.19496/) (2025)
- [NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning](https://mlsanity.com/paper/2601.19947/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) fail to learn effectively under label noise and have been shown to memorize random labels which affect their generalization performance. We consider learning in isolation, using one-hot encoded labels as the sole source of supervision, and a lack of regularization to discourage memorization as the major shortcomings of the standard training procedure. Thus, we propose Noisy Concurrent Training (NCT) which leverages collaborative learning to use the consensus between two models as an additional source of supervision. Furthermore, inspired by trial-to-trial variability in the brain, we propose a counter-intuitive regularization technique, target variability, which entails randomly changing the labels of a percentage of training samples in each batch as a deterrent to memorization and over-generalization in DNNs. Target variability is applied independently to each model to keep them diverged and avoid the confirmation bias. As DNNs tend to prioritize learning simple patterns first before memorizing the noisy labels, we employ a dynamic learning scheme whereby as the training progresses, the two models increasingly rely more on their consensus. NCT also progressively increases the target variability to avoid memorization in later stages. We demonstrate the effectiveness of our approach on both synthetic and real-world noisy benchmark datasets.