# Data Consistency for Weakly Supervised Learning

arXiv: https://arxiv.org/abs/2202.03987 · HTML: https://mlsanity.com/paper/2202.03987/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [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)
- [Joint Optimization Framework for Learning with Noisy Labels](https://mlsanity.com/paper/1803.11364/) (2018)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Harnessing Deep Neural Networks with Logic Rules](https://mlsanity.com/paper/1603.06318/) (2016)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020)
- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [Error-Bounded Correction of Noisy Labels](https://mlsanity.com/paper/2011.10077/) (2020)
- [Learning from Rules Generalizing Labeled Exemplars](https://mlsanity.com/paper/2004.06025/) (2020)
- [Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale](https://mlsanity.com/paper/1812.00417/) (2018)
- [Learning From Noisy Singly-labeled Data](https://mlsanity.com/paper/1712.04577/) (2017)
- [Self-Training with Weak Supervision](https://mlsanity.com/paper/2104.05514/) (2021)
- [Unsupervised Ensemble Learning with Dependent Classifiers](https://mlsanity.com/paper/1510.05830/) (2015)
- [Unsupervised Risk Estimation Using Only Conditional Independence Structure](https://mlsanity.com/paper/1606.05313/) (2016)
- [Constrained Labeling for Weakly Supervised Learning](https://mlsanity.com/paper/2009.07360/) (2020)
- [Optimally Combining Classifiers Using Unlabeled Data](https://mlsanity.com/paper/1503.01811/) (2015)
- [Adversarial Label Learning](https://mlsanity.com/paper/1805.08877/) (2018)
- [Learning from Imperfect Annotations](https://mlsanity.com/paper/2004.03473/) (2020)
- [Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation](https://mlsanity.com/paper/2103.02761/) (2021)
- [Regularized Minimax Conditional Entropy for Crowdsourcing](https://mlsanity.com/paper/1503.07240/) (2015)
- [Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings](https://mlsanity.com/paper/2006.15168/) (2020)
- [Clinical Tagging with Joint Probabilistic Models](https://mlsanity.com/paper/1608.00686/) (2016)

## Built on by (most cited first, 2 of 2)

- [Losses over Labels: Weakly Supervised Learning via Direct Loss Construction](https://mlsanity.com/paper/2212.06921/) (2022)
- [Label Propagation with Weak Supervision](https://mlsanity.com/paper/2210.03594/) (2022)

## Abstract (arXiv)

In many applications, training machine learning models involves using large amounts of human-annotated data. Obtaining precise labels for the data is expensive. Instead, training with weak supervision provides a low-cost alternative. We propose a novel weak supervision algorithm that processes noisy labels, i.e., weak signals, while also considering features of the training data to produce accurate labels for training. Our method searches over classifiers of the data representation to find plausible labelings. We call this paradigm data consistent weak supervision. A key facet of our framework is that we are able to estimate labels for data examples low or no coverage from the weak supervision. In addition, we make no assumptions about the joint distribution of the weak signals and true labels of the data. Instead, we use weak signals and the data features to solve a constrained optimization that enforces data consistency among the labels we generate. Empirical evaluation of our method on different datasets shows that it significantly outperforms state-of-the-art weak supervision methods on both text and image classification tasks.