# Unsupervised Estimation of Ensemble Accuracy

arXiv: https://arxiv.org/abs/2311.10940 · HTML: https://mlsanity.com/paper/2311.10940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [DigiFace-1M: 1 Million Digital Face Images for Face Recognition](https://mlsanity.com/paper/2210.02579/) (2022)
- [Dataset Cleaning -- A Cross Validation Methodology for Large Facial Datasets using Face Recognition](https://mlsanity.com/paper/2003.10815/) (2020)

## Abstract (arXiv)

Ensemble learning combines several individual models to obtain a better generalization performance. In this work we present a practical method for estimating the joint power of several classifiers. It differs from existing approaches which focus on "diversity" measures by not relying on labels. This makes it both accurate and practical in the modern setting of unsupervised learning with huge datasets. The heart of the method is a combinatorial bound on the number of mistakes the ensemble is likely to make. The bound can be efficiently approximated in time linear in the number of samples. We relate the bound to actual misclassifications, hence its usefulness as a predictor of performance. We demonstrate the method on popular large-scale face recognition datasets which provide a useful playground for fine-grain classification tasks using noisy data over many classes.