# Diversity regularization in deep ensembles

arXiv: https://arxiv.org/abs/1802.07881 · HTML: https://mlsanity.com/paper/1802.07881/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 11 of 11)

- [DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation](https://mlsanity.com/paper/2101.05544/) (2021)
- [Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping](https://mlsanity.com/paper/2206.03633/) (2022)
- [Verification-Aided Deep Ensemble Selection](https://mlsanity.com/paper/2202.03898/) (2022)
- [Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge](https://mlsanity.com/paper/2206.03165/) (2022)
- [Improving robustness and calibration in ensembles with diversity regularization](https://mlsanity.com/paper/2201.10908/) (2022)
- [Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification](https://mlsanity.com/paper/2304.04042/) (2023)
- [Bayesian Quadrature for Neural Ensemble Search](https://mlsanity.com/paper/2303.08874/) (2023)
- [Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy](https://mlsanity.com/paper/2305.11616/) (2023)
- [MODL: Multilearner Online Deep Learning](https://mlsanity.com/paper/2405.18281/) (2024)
- [Parameter-Efficient Construction of the Rashomon Slice for Concept Bottleneck Models](https://mlsanity.com/paper/2511.19636/) (2025)
- [Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy Images](https://mlsanity.com/paper/2209.01908/) (2022)

## Abstract (arXiv)

Calibrating the confidence of supervised learning models is important for a variety of contexts where the certainty over predictions should be reliable. However, it has been reported that deep neural network models are often too poorly calibrated for achieving complex tasks requiring reliable uncertainty estimates in their prediction. In this work, we are proposing a strategy for training deep ensembles with a diversity function regularization, which improves the calibration property while maintaining a similar prediction accuracy.