# Controlled Dropout for Uncertainty Estimation

arXiv: https://arxiv.org/abs/2205.03109 · HTML: https://mlsanity.com/paper/2205.03109/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 6 May 2022.

## Builds on (most cited first, 20 of 20)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Concrete Dropout](https://mlsanity.com/paper/1705.07832/) (2017)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [R-Drop: Regularized Dropout for Neural Networks](https://mlsanity.com/paper/2106.14448/) (2021)
- [Multiplicative Normalizing Flows for Variational Bayesian Neural Networks](https://mlsanity.com/paper/1703.01961/) (2017)
- [Modelling Uncertainty in Deep Learning for Camera Relocalization](https://mlsanity.com/paper/1509.05909/) (2015)
- [Noisy Natural Gradient as Variational Inference](https://mlsanity.com/paper/1712.02390/) (2017)
- [Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors](https://mlsanity.com/paper/1603.04733/) (2016)
- [Learning Sparse Networks Using Targeted Dropout](https://mlsanity.com/paper/1905.13678/) (2019)
- [Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection](https://mlsanity.com/paper/2012.11840/) (2020)
- [Qualitative Analysis of Monte Carlo Dropout](https://mlsanity.com/paper/2007.01720/) (2020)
- [Improving the repeatability of deep learning models with Monte Carlo dropout](https://mlsanity.com/paper/2202.07562/) (2022)
- [Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization](https://mlsanity.com/paper/2010.05244/) (2020)
- [Wasserstein Dropout](https://mlsanity.com/paper/2012.12687/) (2020)
- [On Batch Normalisation for Approximate Bayesian Inference](https://mlsanity.com/paper/2012.13220/) (2020)
- [A Bayesian Neural Network based on Dropout Regulation](https://mlsanity.com/paper/2102.01968/) (2021)

## Built on by (most cited first, 4 of 4)

- [Through the Lens of Doubt: Robust and Efficient Uncertainty Estimation for Visual Place Recognition](https://mlsanity.com/paper/2510.13464/) (2025)
- [An Uncertainty-Aware Dynamic Decision Framework for Progressive Multi-Omics Integration in Classification Tasks](https://mlsanity.com/paper/2507.01032/) (2025)
- [Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantification](https://mlsanity.com/paper/2509.18155/) (2025)
- [Enhancing Multi-Label Thoracic Disease Diagnosis with Deep Ensemble-Based Uncertainty Quantification](https://mlsanity.com/paper/2511.18839/) (2025)

## Abstract (arXiv)

Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictions, which lack information about uncertainty estimates. Among various methods to enable neural networks to estimate uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short period due to its simplicity. In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations. As such, each layer can take and apply the new dropout layer in the MC method to quantify the uncertainty associated with NN predictions. We conduct experiments on both toy and realistic datasets and compare the results with the MC method using the traditional dropout layer. Performance analysis utilizing uncertainty evaluation metrics corroborates that our dropout layer offers better performance in most cases.