# Masked Contrastive Learning for Anomaly Detection

arXiv: https://arxiv.org/abs/2105.08793 · HTML: https://mlsanity.com/paper/2105.08793/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances](https://mlsanity.com/paper/2007.08176/) (2020)
- [Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem](https://mlsanity.com/paper/1812.05720/) (2018)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [Contrastive Training for Improved Out-of-Distribution Detection](https://mlsanity.com/paper/2007.05566/) (2020)
- [C2AE: Class Conditioned Auto-Encoder for Open-set Recognition](https://mlsanity.com/paper/1904.01198/) (2019)
- [Hybrid Models for Open Set Recognition](https://mlsanity.com/paper/2003.12506/) (2020)
- [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https://mlsanity.com/paper/1809.04758/) (2018)

## Built on by (most cited first, 23 of 23)

- [Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook](https://mlsanity.com/paper/2205.05173/) (2022)
- [Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images](https://mlsanity.com/paper/2109.01303/) (2021)
- [Partitioning Message Passing for Graph Fraud Detection](https://mlsanity.com/paper/2412.00020/) (2024)
- [Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders](https://mlsanity.com/paper/2212.07050/) (2022)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble](https://mlsanity.com/paper/2210.11034/) (2022)
- [OOD Aware Supervised Contrastive Learning](https://mlsanity.com/paper/2310.01942/) (2023)
- [OOD Augmentation May Be at Odds with Open-Set Recognition](https://mlsanity.com/paper/2206.04242/) (2022)
- [SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets](https://mlsanity.com/paper/2309.15245/) (2023)
- [Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling](https://mlsanity.com/paper/2201.06378/) (2022)
- [Deep Federated Anomaly Detection for Multivariate Time Series Data](https://mlsanity.com/paper/2205.04041/) (2022)
- [Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection](https://mlsanity.com/paper/2302.14696/) (2023)
- [Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP](https://mlsanity.com/paper/2310.14849/) (2023)
- [Proxy-based Item Representation for Attribute and Context-aware Recommendation](https://mlsanity.com/paper/2312.06145/) (2023)
- [Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection](https://mlsanity.com/paper/2211.03183/) (2022)
- [Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection](https://mlsanity.com/paper/2302.02598/) (2023)
- [Towards a Unified Framework of Clustering-based Anomaly Detection](https://mlsanity.com/paper/2406.00452/) (2024)
- [Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback](https://mlsanity.com/paper/2210.02841/) (2022)
- [Anomaly Detection via Multi-Scale Contrasted Memory](https://mlsanity.com/paper/2211.09041/) (2022)
- [Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning](https://mlsanity.com/paper/2302.10601/) (2023)
- [AGAD: Adversarial Generative Anomaly Detection](https://mlsanity.com/paper/2304.04211/) (2023)
- [End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection](https://mlsanity.com/paper/2404.02865/) (2024)
- [CroTad: A Contrastive Reinforcement Learning Framework for Online Trajectory Anomaly Detection](https://mlsanity.com/paper/2511.16929/) (2025)

## Abstract (arXiv)

Detecting anomalies is one fundamental aspect of a safety-critical software system, however, it remains a long-standing problem. Numerous branches of works have been proposed to alleviate the complication and have demonstrated their efficiencies. In particular, self-supervised learning based methods are spurring interest due to their capability of learning diverse representations without additional labels. Among self-supervised learning tactics, contrastive learning is one specific framework validating their superiority in various fields, including anomaly detection. However, the primary objective of contrastive learning is to learn task-agnostic features without any labels, which is not entirely suited to discern anomalies. In this paper, we propose a task-specific variant of contrastive learning named masked contrastive learning, which is more befitted for anomaly detection. Moreover, we propose a new inference method dubbed self-ensemble inference that further boosts performance by leveraging the ability learned through auxiliary self-supervision tasks. By combining our models, we can outperform previous state-of-the-art methods by a significant margin on various benchmark datasets.