# Deep Transductive Outlier Detection

arXiv: https://arxiv.org/abs/2404.03495 · HTML: https://mlsanity.com/paper/2404.03495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 18 of 18)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Test-Time Training with Masked Autoencoders](https://mlsanity.com/paper/2209.07522/) (2022)
- [Classification-Based Anomaly Detection for General Data](https://mlsanity.com/paper/2005.02359/) (2020)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [Neural Transformation Learning for Deep Anomaly Detection Beyond Images](https://mlsanity.com/paper/2103.16440/) (2021)
- [On Diffusion Modeling for Anomaly Detection](https://mlsanity.com/paper/2305.18593/) (2023)
- [Meta-survey on outlier and anomaly detection](https://mlsanity.com/paper/2312.07101/) (2023)
- [Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection](https://mlsanity.com/paper/1910.09754/) (2019)
- [UADB: Unsupervised Anomaly Detection Booster](https://mlsanity.com/paper/2306.01997/) (2023)
- [Self-Tuning Self-Supervised Image Anomaly Detection](https://mlsanity.com/paper/2306.12033/) (2023)
- [Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning](https://mlsanity.com/paper/2209.15570/) (2022)
- [Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities](https://mlsanity.com/paper/2308.14380/) (2023)

## Built on by (most cited first, 1 of 1)

- [Rare anomalies require large datasets: About proving the existence of anomalies](https://mlsanity.com/paper/2508.09894/) (2025)

## Abstract (arXiv)

Outlier detection (OD) is one of the core challenges in machine learning. Transductive learning, which leverages test data during training, has shown promise in related machine learning tasks, yet remains largely unexplored for modern OD. We present Doust, the first end-to-end transductive deep learning algorithm for outlier detection, which explicitly leverages unlabeled test data to boost accuracy. On the comprehensive ADBench benchmark, Doust achieves an average ROC-AUC of 89%, outperforming all 21 competitors by roughly 10%. Our analysis identifies both the potential and a limitation of transductive OD: while performance gains can be substantial in favorable conditions, very low contamination rates can hinder improvements unless the dataset is sufficiently large.