# Meta Soft Label Generation for Noisy Labels

arXiv: https://arxiv.org/abs/2007.05836 · HTML: https://mlsanity.com/paper/2007.05836/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 21 of 21)

- [Breaking the Dilemma of Medical Image-to-image Translation](https://mlsanity.com/paper/2110.06465/) (2021)
- [Gradient-based Bi-level Optimization for Deep Learning: A Survey](https://mlsanity.com/paper/2207.11719/) (2022)
- [Noise-resistant Deep Metric Learning with Ranking-based Instance Selection](https://mlsanity.com/paper/2103.16047/) (2021)
- [Learning from Noisy Labels with Decoupled Meta Label Purifier](https://mlsanity.com/paper/2302.06810/) (2023)
- [PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels](https://mlsanity.com/paper/2110.11809/) (2021)
- [Generalized Data Weighting via Class-level Gradient Manipulation](https://mlsanity.com/paper/2111.00056/) (2021)
- [L2B: Learning to Bootstrap Robust Models for Combating Label Noise](https://mlsanity.com/paper/2202.04291/) (2022)
- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
- [Enhanced Meta Label Correction for Coping with Label Corruption](https://mlsanity.com/paper/2305.12961/) (2023)
- [MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels](https://mlsanity.com/paper/2103.10869/) (2021)
- [Adaptive Mixing of Auxiliary Losses in Supervised Learning](https://mlsanity.com/paper/2202.03250/) (2022)
- [Curvature Learning for Generalization of Hyperbolic Neural Networks](https://mlsanity.com/paper/2508.17232/) (2025)
- [CNTN: Cyclic Noise-tolerant Network for Gait Recognition](https://mlsanity.com/paper/2210.06910/) (2022)
- [Selective classification using a robust meta-learning approach](https://mlsanity.com/paper/2212.05987/) (2022)
- [Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2408.16029/) (2024)
- [Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning](https://mlsanity.com/paper/2208.08132/) (2022)
- [kNet: A Deep kNN Network To Handle Label Noise](https://mlsanity.com/paper/2107.09735/) (2021)
- [Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering](https://mlsanity.com/paper/2108.01431/) (2021)
- [Deep Learning with Label Noise: A Hierarchical Approach](https://mlsanity.com/paper/2205.14299/) (2022)
- [FlexSSL : A Generic and Efficient Framework for Semi-Supervised Learning](https://mlsanity.com/paper/2312.16892/) (2023)
- [Robust Testing for Deep Learning using Human Label Noise](https://mlsanity.com/paper/2412.00244/) (2024)

## Abstract (arXiv)

The existence of noisy labels in the dataset causes significant performance degradation for deep neural networks (DNNs). To address this problem, we propose a Meta Soft Label Generation algorithm called MSLG, which can jointly generate soft labels using meta-learning techniques and learn DNN parameters in an end-to-end fashion. Our approach adapts the meta-learning paradigm to estimate optimal label distribution by checking gradient directions on both noisy training data and noise-free meta-data. In order to iteratively update soft labels, meta-gradient descent step is performed on estimated labels, which would minimize the loss of noise-free meta samples. In each iteration, the base classifier is trained on estimated meta labels. MSLG is model-agnostic and can be added on top of any existing model at hand with ease. We performed extensive experiments on CIFAR10, Clothing1M and Food101N datasets. Results show that our approach outperforms other state-of-the-art methods by a large margin.