# Beyond Hard Labels: Investigating data label distributions

arXiv: https://arxiv.org/abs/2207.06224 · HTML: https://mlsanity.com/paper/2207.06224/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Early-Learning Regularization Prevents Memorization of Noisy Labels](https://mlsanity.com/paper/2007.00151/) (2020)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations](https://mlsanity.com/paper/2110.12088/) (2021)
- [Human uncertainty makes classification more robust](https://mlsanity.com/paper/1908.07086/) (2019)
- [Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels](https://mlsanity.com/paper/2101.05022/) (2021)
- [Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering](https://mlsanity.com/paper/2012.01768/) (2020)
- [A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering](https://mlsanity.com/paper/2106.16209/) (2021)
- [On Data-centric Myths](https://mlsanity.com/paper/2111.11514/) (2021)

## Built on by (most cited first, 7 of 7)

- [Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels](https://mlsanity.com/paper/2401.15500/) (2024)
- [Predict the Next Word: Humans exhibit uncertainty in this task and language models _____](https://mlsanity.com/paper/2402.17527/) (2024)
- [Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Quality](https://mlsanity.com/paper/2305.12811/) (2023)
- [Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall](https://mlsanity.com/paper/2309.13938/) (2023)
- [Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study](https://mlsanity.com/paper/2207.10970/) (2022)
- [Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation](https://mlsanity.com/paper/2306.12189/) (2023)
- [Learning from Ambiguous Data with Hard Labels](https://mlsanity.com/paper/2501.01844/) (2025)

## Abstract (arXiv)

High-quality data is a key aspect of modern machine learning. However, labels generated by humans suffer from issues like label noise and class ambiguities. We raise the question of whether hard labels are sufficient to represent the underlying ground truth distribution in the presence of these inherent imprecision. Therefore, we compare the disparity of learning with hard and soft labels quantitatively and qualitatively for a synthetic and a real-world dataset. We show that the application of soft labels leads to improved performance and yields a more regular structure of the internal feature space.