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archon159 avatar

archon159/elsa

0
View on GitHub↗
11 stars·1 fork·Python·MIT·9 views

Elsa

Elsa: Energy-based Learning forSemi-supervised Anomaly Detection Official PyTorch implementation of "Elsa: Energy-based Learning forSemi-supervised Anomaly Detection" (BMVC 2021) by Sungwon Han, Hyeonho Song, Seungeon Lee, Sungwon Park, Meeyoung Cha.

Features

  • Image Classification - Energy-based learning for semi-supervised anomaly detection.

Star history

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Open-source alternatives to Elsa

Similar open-source projects, ranked by how many features they share with Elsa.
  • demonzyj56/e3outlierdemonzyj56 avatar

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  • hendrycks/ss-oodhendrycks avatar

    hendrycks/ss-ood

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    Self-Supervised Learning for OOD Detection (NeurIPS 2019)

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  • jacenkow/mmbtJ

    jacenkow/mmbt

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    Code for the paper:

    View on GitHub↗0
  • alinlab/csialinlab avatar

    alinlab/CSI

    285View on GitHub↗

    CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)

    Python
    View on GitHub↗285
See all 10 alternatives to Elsa→

Frequently asked questions

What does archon159/elsa do?

Elsa: Energy-based Learning forSemi-supervised Anomaly Detection Official PyTorch implementation of "Elsa: Energy-based Learning forSemi-supervised Anomaly Detection" (BMVC 2021) by Sungwon Han, Hyeonho Song, Seungeon Lee, Sungwon Park, Meeyoung Cha.

What are the main features of archon159/elsa?

The main features of archon159/elsa are: Image Classification.

What are some open-source alternatives to archon159/elsa?

Open-source alternatives to archon159/elsa include: alinlab/csi — CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020). demonzyj56/e3outlier — E3Outlier: Effective End-to-end Unsupervised Outlier Detection. hendrycks/ss-ood — Self-Supervised Learning for OOD Detection (NeurIPS 2019). jacenkow/mmbt — Code for the paper:. khalooei/alocc-cvpr2018 — Adversarially Learned One-Class Classifier for Novelty Detection (ALOCC). orippler/gaussian-ad-mvtec — Code underlying our publication "Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly…