# Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection

arXiv: https://arxiv.org/abs/1910.09754 · HTML: https://mlsanity.com/paper/1910.09754/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges](https://mlsanity.com/paper/2210.12089/) (2022)
- [Are we certain it's anomalous?](https://mlsanity.com/paper/2211.09224/) (2022)
- [Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble](https://mlsanity.com/paper/2103.11678/) (2021)
- [Deep Transductive Outlier Detection](https://mlsanity.com/paper/2404.03495/) (2024)
- [How to boost autoencoders?](https://mlsanity.com/paper/2110.15307/) (2021)
- [Cross-domain Robust Deepfake Bias Expansion Network for Face Forgery Detection](https://mlsanity.com/paper/2310.05124/) (2023)
- [Sparse Autoencoders for Interpretable Out-of-Distribution Detection](https://mlsanity.com/paper/2607.12094/) (2026)

## Abstract (arXiv)

Autoencoders, as a dimensionality reduction technique, have been recently applied to outlier detection. However, neural networks are known to be vulnerable to overfitting, and therefore have limited potential in the unsupervised outlier detection setting. Current approaches to ensemble-based autoencoders do not generate a sufficient level of diversity to avoid the overfitting issue. To overcome the aforementioned limitations we develop a Boosting-based Autoencoder Ensemble approach (in short, BAE). BAE is an unsupervised ensemble method that, similarly to the boosting approach, builds an adaptive cascade of autoencoders to achieve improved and robust results. BAE trains the autoencoder components sequentially by performing a weighted sampling of the data, aimed at reducing the amount of outliers used during training, and at injecting diversity in the ensemble. We perform extensive experiments and show that the proposed methodology outperforms state-of-the-art approaches under a variety of conditions.