# q-Space Novelty Detection with Variational Autoencoders

arXiv: https://arxiv.org/abs/1806.02997 · HTML: https://mlsanity.com/paper/1806.02997/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 22 of 22)

- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection](https://mlsanity.com/paper/2211.11317/) (2022)
- [Student-Teacher Feature Pyramid Matching for Anomaly Detection](https://mlsanity.com/paper/2103.04257/) (2021)
- [Inpainting Transformer for Anomaly Detection](https://mlsanity.com/paper/2104.13897/) (2021)
- [Rotation-Equivariant Deep Learning for Diffusion MRI](https://mlsanity.com/paper/2102.06942/) (2021)
- [Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems](https://mlsanity.com/paper/2108.11800/) (2021)
- [Towards Unsupervised Open World Semantic Segmentation](https://mlsanity.com/paper/2201.01073/) (2022)
- [Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection](https://mlsanity.com/paper/2210.07548/) (2022)
- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [A SAM-guided Two-stream Lightweight Model for Anomaly Detection](https://mlsanity.com/paper/2402.19145/) (2024)
- [HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization](https://mlsanity.com/paper/2208.03486/) (2022)
- [Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination](https://mlsanity.com/paper/2411.09558/) (2024)
- [Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition](https://mlsanity.com/paper/2103.04279/) (2021)
- [Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder](https://mlsanity.com/paper/2210.09959/) (2022)
- [Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots](https://mlsanity.com/paper/2403.03563/) (2024)
- [Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patients](https://mlsanity.com/paper/2101.06772/) (2021)
- [Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields](https://mlsanity.com/paper/2107.06028/) (2021)
- [Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems](https://mlsanity.com/paper/2307.13419/) (2023)
- [Novelty-based Generalization Evaluation for Traffic Light Detection](https://mlsanity.com/paper/2201.00531/) (2022)
- [A Hierarchically Feature Reconstructed Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2405.09148/) (2024)
- [Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment](https://mlsanity.com/paper/2409.00880/) (2024)
- [Disentangled and Distilled Encoder for Out-of-Distribution Reasoning with Rademacher Guarantees](https://mlsanity.com/paper/2512.10522/) (2025)

## Abstract (arXiv)

In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders (VAEs) on normal data. Since abnormal samples are not used during training, we define novelty metrics based on the (partially complementary) assumptions that the VAE is less capable of reconstructing abnormal samples well; that abnormal samples more strongly violate the VAE regularizer; and that abnormal samples differ from normal samples not only in input-feature space, but also in the VAE latent space and VAE output. These approaches, combined with various possibilities of using (e.g. sampling) the probabilistic VAE to obtain scalar novelty scores, yield a large family of methods. We apply these methods to magnetic resonance imaging, namely to the detection of diffusion-space (q-space) abnormalities in diffusion MRI scans of multiple sclerosis patients, i.e. to detect multiple sclerosis lesions without using any lesion labels for training. Many of our methods outperform previously proposed q-space novelty detection methods. We also evaluate the proposed methods on the MNIST handwritten digits dataset and show that many of them are able to outperform the state of the art.