Deep evolving semi-supervised anomaly detection | Paper Citation Record

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This paper cites Anomaly detection: A survey.

Deep evolving semi-supervised anomaly detection Anomaly detection: A survey

Reference 1

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Observation c8f7a10f-2070-420e-87c7-1837ee1549ab · outbound

This paper cites Robust one-class svm for fault detection.

Deep evolving semi-supervised anomaly detection Robust one-class svm for fault detection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:29.866799Z

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Observation 4034061d-db9c-406a-9678-6e31aaa73a6e · outbound

This paper cites One-class classification for heart disease diagnosis.

Deep evolving semi-supervised anomaly detection One-class classification for heart disease diagnosis

Reference 3

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Observation 9645d278-9101-44b1-93e7-7176924cb828 · outbound

This paper cites Lifelong machine learning: a paradigm for continuous learning.Frontiers of Computer Science, 11(3):359–361, 2017.

Deep evolving semi-supervised anomaly detection Lifelong machine learning: a paradigm for continuous learning.Frontiers of Computer Science, 11(3):359–361, 2017

Reference 4

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Observation 39d4cc14-c30f-4420-b960-40a003991f51 · outbound

This paper cites Deep Semi-Supervised Anomaly Detection.

Deep evolving semi-supervised anomaly detection Deep Semi-Supervised Anomaly Detection

Reference 5

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Observation 30cbf974-d3c1-4e9c-911b-c6e67283f361 · outbound

This paper cites Semi-supervised learning (chapelle, o.

Deep evolving semi-supervised anomaly detection Semi-supervised learning (chapelle, o

Reference 6

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This paper cites International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines.

Deep evolving semi-supervised anomaly detection International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines

Reference 7

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Observation 292a4ba1-2ab9-4ac2-8d45-adc87f9a1848 · outbound

This paper cites Learning on the job: Online lifelong and continual learning.

Deep evolving semi-supervised anomaly detection Learning on the job: Online lifelong and continual learning

Reference 8

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Observation 62f2c0e4-7a92-4aad-bdbd-edaf21b29cbd · outbound

This paper cites Catastrophic forgetting in connectionist networks.

Deep evolving semi-supervised anomaly detection Catastrophic forgetting in connectionist networks

Reference 9

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Observation a11a2556-b721-4812-8b73-b0b9d09890eb · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Deep evolving semi-supervised anomaly detection Overcoming catastrophic forgetting in neural networks

Reference 10

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Observation 7edbf5d2-750b-4e26-85ba-2398bc3b3fcd · outbound

This paper cites Continual learning for anomaly detection with variational autoen- coder.

Deep evolving semi-supervised anomaly detection Continual learning for anomaly detection with variational autoen- coder

Reference 11

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Observation 484dc15f-de46-47ca-a382-b34817620fa9 · outbound

This paper cites Beyond Supervised Continual Learning: a Review.

Deep evolving semi-supervised anomaly detection Beyond Supervised Continual Learning: a Review

Reference 12

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Observation a3a83a69-d993-44f2-b842-11930bb8b102 · outbound

This paper cites Avalanche: an end-to-end library for continual learning.

Deep evolving semi-supervised anomaly detection Avalanche: an end-to-end library for continual learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:29.485811Z

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Observation 6e9a7b1e-2656-4e8a-92a9-e420ab34b174 · outbound

This paper cites Continuum: Simple Management of Complex Continual Learning Scenarios.

Deep evolving semi-supervised anomaly detection Continuum: Simple Management of Complex Continual Learning Scenarios

Reference 14

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Observation 9b4e9b07-1276-432f-b591-62acc99088db · outbound

This paper cites Sequoia: A Software Framework to Unify Continual Learning Research.

Deep evolving semi-supervised anomaly detection Sequoia: A Software Framework to Unify Continual Learning Research

Reference 15

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This paper cites The rise of ’big data’ on cloud computing: Review and open research issues.

Deep evolving semi-supervised anomaly detection The rise of ’big data’ on cloud computing: Review and open research issues

Reference 16

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Observation d50215e1-32db-45d2-bea3-ac8513e0140a · outbound

This paper cites An Overview of Deep Semi-Supervised Learning.

Deep evolving semi-supervised anomaly detection An Overview of Deep Semi-Supervised Learning

Reference 17

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Observation 0e18c9ee-0cc3-47db-94b9-360d0476f121 · outbound

This paper cites Information theory and statistics.

Deep evolving semi-supervised anomaly detection Information theory and statistics

Reference 18

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Observation e09a0804-1efe-4e5c-9cbf-509d049740b1 · outbound

This paper cites An introduction to variational autoencoders.

Deep evolving semi-supervised anomaly detection An introduction to variational autoencoders

Reference 19

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Observation 63a35cd2-0dd8-47da-99ac-2c4f94210a31 · outbound

This paper cites Generative adversarial networks.

Deep evolving semi-supervised anomaly detection Generative adversarial networks

Reference 20

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Observation aac88aab-c66d-4d23-880b-f4b9b61c9c17 · outbound

This paper cites Semi-Supervised Learning with Generative Adversarial Networks.

Deep evolving semi-supervised anomaly detection Semi-Supervised Learning with Generative Adversarial Networks

Reference 21

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Observation b79aa2ef-1e5b-48ea-964a-3c601c756dfe · outbound

This paper cites Experi- ence replay for continual learning.

Deep evolving semi-supervised anomaly detection Experi- ence replay for continual learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:29.299790Z

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This paper cites Continual learning through one-class classifica- tion using vae.

Deep evolving semi-supervised anomaly detection Continual learning through one-class classifica- tion using vae

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:29.222515Z

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Observation c3fbcf2c-4987-40a8-a72c-322ce2e3c6b2 · outbound

This paper cites Boovae: Boosting approach for continual learning of vae.

Deep evolving semi-supervised anomaly detection Boovae: Boosting approach for continual learning of vae

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:29.060907Z

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Observation 27d9faaa-a471-45b8-83bf-e8e748764116 · outbound

This paper cites Continual learning with deep generative replay.

Deep evolving semi-supervised anomaly detection Continual learning with deep generative replay

Reference 25

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Observation 229638e5-f47c-4274-b7a9-a095288a6653 · outbound

This paper cites Coresets for nonparametric estimation-the case of dp-means.

Deep evolving semi-supervised anomaly detection Coresets for nonparametric estimation-the case of dp-means

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.998390Z

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Observation 9ac99595-9d7a-4a29-a703-b5321c04b74f · outbound

This paper cites Binplay: A binary latent autoencoder for generative replay continual learning.

Deep evolving semi-supervised anomaly detection Binplay: A binary latent autoencoder for generative replay continual learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.984493Z

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Observation a1197d2b-d4ed-4609-8a31-675b19cae2f1 · outbound

This paper cites Metric learning for large scale image classification: Generalizing to new classes at near-zero cost.

Deep evolving semi-supervised anomaly detection Metric learning for large scale image classification: Generalizing to new classes at near-zero cost

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.969112Z

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Observation fa6ed285-5b58-434d-925b-94c809672cff · outbound

This paper cites Unified probabilistic deep continual learning through generative replay and open set recognition.

Deep evolving semi-supervised anomaly detection Unified probabilistic deep continual learning through generative replay and open set recognition

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.813452Z

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Observation 0b8e8c21-0f04-4abe-93c8-f93a88e3bc2a · outbound

This paper cites Semi-supervised anomaly detection algorithms: A comparative summary and future research directions.

Deep evolving semi-supervised anomaly detection Semi-supervised anomaly detection algorithms: A comparative summary and future research directions

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.736579Z

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Observation 9f95c638-31eb-4b85-9bf4-3f1213e1d352 · outbound

This paper cites Bagging-randomminer: A one-class classifier for file access-based masquerade detection.Machine Vision and Applications, 30(5):959–974, 2019.

Deep evolving semi-supervised anomaly detection Bagging-randomminer: A one-class classifier for file access-based masquerade detection.Machine Vision and Applications, 30(5):959–974, 2019

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.693999Z

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Observation c7c98966-f53a-44c7-a0d3-7e0aa3f5bd00 · outbound

This paper cites Towards anomaly detectors that learn continuously.

Deep evolving semi-supervised anomaly detection Towards anomaly detectors that learn continuously

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.656076Z

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Observation 7df4d0a0-5ca3-47bd-92e4-a76ccc2bdc82 · outbound

This paper cites Arcade: A rapid continual anomaly detector.

Deep evolving semi-supervised anomaly detection Arcade: A rapid continual anomaly detector

Reference 33

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Observation 6ef96a50-91c0-4b99-b5d8-2c6831211a5f · outbound

This paper cites Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data.

Deep evolving semi-supervised anomaly detection Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-12T04:58:27.791037Z

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Observation 6b3d4ebc-f9c4-4c17-aee1-595da843b6b3 · outbound

This paper cites Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning.

Deep evolving semi-supervised anomaly detection Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning

Reference 35

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Observation dce3a926-7b0f-4a69-93be-115eacaf7a21 · outbound

This paper cites Semi-supervised learning literature survey.

Deep evolving semi-supervised anomaly detection Semi-supervised learning literature survey

Reference 36

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Observation 165d545a-6b6f-445d-b832-e6f655cc9cf8 · outbound

This paper cites All versus one: an empirical comparison on retrained and incremental machine learning for modeling performance of adaptable software.

Deep evolving semi-supervised anomaly detection All versus one: an empirical comparison on retrained and incremental machine learning for modeling performance of adaptable software

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.303403Z

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Observation 90133154-6b54-4dbf-8da9-e978c64bb777 · outbound

This paper cites Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders.

Deep evolving semi-supervised anomaly detection Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders

Reference 38

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Observation f83c3082-4f76-4862-a3d4-84682cd8b1f0 · outbound

This paper cites Variational inference: A review for statisticians.

Deep evolving semi-supervised anomaly detection Variational inference: A review for statisticians

Reference 39

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Observation 6b4e9f3e-0421-4e91-b8f3-784d7ea81346 · outbound

This paper cites Stochastic variational inference.

Deep evolving semi-supervised anomaly detection Stochastic variational inference

Reference 40

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Observation f74d4e6f-6961-4b87-b365-c37168bc3d3d · outbound

This paper cites Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function.

Deep evolving semi-supervised anomaly detection Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function

Reference 41

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Observation 5c905efd-6a59-402a-86e6-38a1d8a0529f · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.

Deep evolving semi-supervised anomaly detection beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:28.245817Z

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Observation 1dd8b754-ed24-47ca-98eb-6c984c3361cf · outbound

This paper cites Ladder variational autoencoders.

Deep evolving semi-supervised anomaly detection Ladder variational autoencoders

Reference 43

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Observation 4cf6384a-ee2b-42c6-9034-78072db8f8b5 · outbound

This paper cites Variational autoencoder based anomaly detection using recon- struction probability.

Deep evolving semi-supervised anomaly detection Variational autoencoder based anomaly detection using recon- struction probability

Reference 44

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Observation bc72b7bc-b897-4e60-bb94-39825d72dec9 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].

Deep evolving semi-supervised anomaly detection The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 45

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This paper cites an unresolved cited work.

Deep evolving semi-supervised anomaly detection Unresolved cited work

Reference 46

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Observation fe6c04d2-e4e6-4244-a293-872ee1dbb9be · outbound

This paper cites Learning multiple layers of features from tiny images.

Deep evolving semi-supervised anomaly detection Learning multiple layers of features from tiny images

Reference 47

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This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Deep evolving semi-supervised anomaly detection Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 48

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Observation 42986a0f-b4ac-4d44-9630-705482017643 · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Deep evolving semi-supervised anomaly detection AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 49

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Observation b73a5d39-9637-4ca0-8f0d-be0c3bd767b6 · outbound

This paper cites The use of the area under the roc curve in the evaluation of machine learning algorithms.

Deep evolving semi-supervised anomaly detection The use of the area under the roc curve in the evaluation of machine learning algorithms

Reference 50

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Observation 883509aa-9532-432f-9bd7-d9fc475f4608 · outbound

This paper cites Svd-gan for real-time unsupervised video anomaly detection.

Deep evolving semi-supervised anomaly detection Svd-gan for real-time unsupervised video anomaly detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:27.998810Z

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Observation 5ed3d29c-acdf-4860-aaf0-f040a8a291aa · outbound

This paper cites Do Deep Convolutional Nets Really Need to be Deep and Convolutional?.

Deep evolving semi-supervised anomaly detection Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

Reference 52

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Observation ca3fc94c-5c46-480f-b1f7-9b0a32ace033 · outbound

This paper cites Disentangling disentan- glement in variational autoencoders.

Deep evolving semi-supervised anomaly detection Disentangling disentan- glement in variational autoencoders

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:58:27.950744Z

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