# Learning from Stochastic Labels

arXiv: https://arxiv.org/abs/2302.00299 · HTML: https://mlsanity.com/paper/2302.00299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 68 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [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)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Dimensionality-Driven Learning with Noisy Labels](https://mlsanity.com/paper/1806.02612/) (2018)
- [Open-World Semi-Supervised Learning](https://mlsanity.com/paper/2102.03526/) (2021)
- [NLNL: Negative Learning for Noisy Labels](https://mlsanity.com/paper/1908.07387/) (2019)
- [Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective](https://mlsanity.com/paper/2003.10780/) (2020)
- [Progressive Identification of True Labels for Partial-Label Learning](https://mlsanity.com/paper/2002.08053/) (2020)
- [Learning with Biased Complementary Labels](https://mlsanity.com/paper/1711.09535/) (2017)
- [Provably Consistent Partial-Label Learning](https://mlsanity.com/paper/2007.08929/) (2020)
- [Learning from Complementary Labels](https://mlsanity.com/paper/1705.07541/) (2017)
- [Semi-Supervised Learning with Normalizing Flows](https://mlsanity.com/paper/1912.13025/) (2019)
- [Leveraged Weighted Loss for Partial Label Learning](https://mlsanity.com/paper/2106.05731/) (2021)
- [Complementary-Label Learning for Arbitrary Losses and Models](https://mlsanity.com/paper/1810.04327/) (2018)
- [On Symmetric Losses for Learning from Corrupted Labels](https://mlsanity.com/paper/1901.09314/) (2019)
- [Learning with Multiple Complementary Labels](https://mlsanity.com/paper/1912.12927/) (2019)
- [Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels](https://mlsanity.com/paper/2007.02235/) (2020)
- [Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning](https://mlsanity.com/paper/1705.01708/) (2017)
- [Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images](https://mlsanity.com/paper/2112.12004/) (2021)
- [Generative-Discriminative Complementary Learning](https://mlsanity.com/paper/1904.01612/) (2019)
- [Tackling Long-Tailed Category Distribution Under Domain Shifts](https://mlsanity.com/paper/2207.10150/) (2022)
- [Class-Imbalanced Complementary-Label Learning via Weighted Loss](https://mlsanity.com/paper/2209.14189/) (2022)
- [The information-theoretic value of unlabeled data in semi-supervised learning](https://mlsanity.com/paper/1901.05515/) (2019)
- [Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances](https://mlsanity.com/paper/1305.0103/) (2013)

## Abstract (arXiv)

Annotating multi-class instances is a crucial task in the field of machine learning. Unfortunately, identifying the correct class label from a long sequence of candidate labels is time-consuming and laborious. To alleviate this problem, we design a novel labeling mechanism called stochastic label. In this setting, stochastic label includes two cases: 1) identify a correct class label from a small number of randomly given labels; 2) annotate the instance with None label when given labels do not contain correct class label. In this paper, we propose a novel suitable approach to learn from these stochastic labels. We obtain an unbiased estimator that utilizes less supervised information in stochastic labels to train a multi-class classifier. Additionally, it is theoretically justifiable by deriving the estimation error bound of the proposed method. Finally, we conduct extensive experiments on widely-used benchmark datasets to validate the superiority of our method by comparing it with existing state-of-the-art methods.