mlsanityData to 7 Oct 2026

Anomalies, Representations, and Self-Supervision

arXiv 2301.04660 ↗First posted 11 Jan 2023hep-ph cs.LG hep-excited by 10

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Abstract (arXiv)

We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way. It uses a permutation-invariant Transformer Encoder architecture to map the objects measured in a collider event to the representation space, where the data augmentations define a representation space which is sensitive to potential anomalous features. An AutoEncoder trained on background representations then computes anomaly scores for a variety of signals in the representation space. With AnomalyCLR we find significant improvements on performance metrics for all signals when compared to the raw data baseline.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 29 of its 76 references that are in the map. First posted on arXiv 11 Jan 2023.

Builds on

29 of 76 references.

References · 29

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  3. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  4. 2022Particle Transformer for Jet Taggingcited by 95
  5. 2021Mean-Shifted Contrastive Loss for Anomaly Detectioncited by 87
  6. 2020Anomaly Detection with Density Estimationcited by 71
  7. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  8. 2020Anomaly Detection With Conditional Variational Autoencoderscited by 46
  9. 2017Classification without labels: Learning from mixed samples in high energy physicscited by 42
  10. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 40
  11. 2020Variational Autoencoders for Anomalous Jet Taggingcited by 33
  12. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 28
17 more
  1. 2020Simulation Assisted Likelihood-free Anomaly Detectioncited by 27
  2. 2022PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physicscited by 26
  3. 2022Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlookcited by 24
  4. 2021Better Latent Spaces for Better Autoencoderscited by 20
  5. 2021Bump Hunting in Latent Spacecited by 19
  6. 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
  7. 2021Online-compatible Unsupervised Non-resonant Anomaly Detectioncited by 15
  8. 2022Lorentz group equivariant autoencoderscited by 15
  9. 2021Self-supervised similarity search for large scientific datasetscited by 11
  10. 2021Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distancecited by 10
  11. 2021Autoencoders for Semivisible Jet Detectioncited by 9
  12. 2021Mining for Strong Gravitational Lenses with Self-supervised Learningcited by 8
  13. 2022IRC-safe Graph Autoencoder for unsupervised anomaly detectioncited by 8
  14. 2022Null Hypothesis Test for Anomaly Detectioncited by 7
  15. 2021Estimating Galactic Distances From Images Using Self-supervised Representation Learningcited by 4
  16. 2022Invariant Representation Driven Neural Classifier for Anti-QCD Jet Taggingcited by 4
  17. 2022Resonant Anomaly Detection with Multiple Reference Datasetscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 2; 2026: 2.

10 papers.

  1. 2024Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Modelscited by 44
  2. 2024OmniJet-α: The first cross-task foundation model for particle physicscited by 41
  3. 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
  4. 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimationcited by 10
  5. 2025Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliderscited by 8
  6. 2026EveNet: A Foundation Model for Particle Collision Data Analysiscited by 8
  7. 2023Improving the performance of weak supervision searches using transfer and meta-learningcited by 2
  8. 2024DLScanner: A parameter space scanner package assisted by deep learning methodscited by 1
  9. 2025Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statisticscited by 1
  10. 2026Contrastive Learning for Interpretable Anomaly Detection at Collider Experimentscited by 0