Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection | Paper Citation Record

Observation cddebb74-d1b1-400e-b143-f6b9739a2427 · outbound

This paper cites Panda: Adapting pretrained features for anomaly detection and segmentation,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Panda: Adapting pretrained features for anomaly detection and segmentation,

Reference 1

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Observation 355f3d13-5eb4-4ba1-95ff-76d54f3947eb · outbound

This paper cites Mean-shifted contrastive loss for anomaly de- tection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Mean-shifted contrastive loss for anomaly de- tection,

Reference 2

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Observation acfb642d-4aae-48ba-9b88-5b8b54b08755 · outbound

This paper cites Robust one-class clas- sification using deep kernel spectral regression,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Robust one-class clas- sification using deep kernel spectral regression,

Reference 3

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Observation 7b178dee-8b60-468b-b00c-5310d516887e · outbound

This paper cites Support vector data description,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Support vector data description,

Reference 4

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Observation 63dfb363-468f-4710-9303-0af003c8a94f · outbound

This paper cites Deep one-class classification,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Deep one-class classification,

Reference 5

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Observation 49a3e98a-a169-417e-afda-d0a6c04e7840 · outbound

This paper cites Deep semi-supervised anomaly detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Deep semi-supervised anomaly detection,

Reference 6

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Observation 5b889bcd-47bc-4dda-b3fc-e5a4b1408998 · outbound

This paper cites Dasvdd: Deep autoencoding support vector data descriptor for anomaly detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Dasvdd: Deep autoencoding support vector data descriptor for anomaly detection,

Reference 7

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Observation d70e66c0-a32b-4145-bfb8-fedbb1e3b155 · outbound

This paper cites Deep multi-sphere support vector data description based on disentangled representation learning,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Deep multi-sphere support vector data description based on disentangled representation learning,

Reference 8

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Observation db253d60-6c82-40e2-b767-f4aca019c74b · outbound

This paper cites Large-margin multiple kernelℓ p-svdd using frank–wolfe algorithm for novelty detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Large-margin multiple kernelℓ p-svdd using frank–wolfe algorithm for novelty detection,

Reference 9

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Observation 73e84118-ab8c-4421-ac07-9aad23336213 · outbound

This paper cites Using the nystr ¨om method to speed up kernel machines,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Using the nystr ¨om method to speed up kernel machines,

Reference 10

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Observation b1f6a1f0-c89b-4cd6-ac9b-ce385136074e · outbound

This paper cites Random features for large-scale kernel machines,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Random features for large-scale kernel machines,

Reference 11

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Observation 584f82ee-29fc-4870-bb25-d9fc42fdef7e · outbound

This paper cites Quasi-monte carlo feature maps for shift-invariant kernels,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Quasi-monte carlo feature maps for shift-invariant kernels,

Reference 12

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Observation 3ab9d466-716b-41f0-95fa-01492a35316b · outbound

This paper cites Orthogonal random features,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Orthogonal random features,

Reference 13

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Observation f37576ae-c4ab-43e5-88cd-a1204ba09602 · outbound

This paper cites Fastfood - computing hilbert space expansions in loglinear time,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Fastfood - computing hilbert space expansions in loglinear time,

Reference 14

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Observation ba7f0977-2d9b-4e1c-ac69-d7a6539e896e · outbound

This paper cites Randomly pivoted cholesky: Practical approximation of a kernel matrix with few entry evaluations,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Randomly pivoted cholesky: Practical approximation of a kernel matrix with few entry evaluations,

Reference 15

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Observation 3bbac245-f456-4402-be37-fbe182e97439 · outbound

This paper cites Deep large-margin lp-svdd with cnn feature learning for novelty detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Deep large-margin lp-svdd with cnn feature learning for novelty detection,

Reference 16

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Observation c21298ec-9683-4181-9645-45d0e84040b8 · outbound

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

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Learning multiple layers of features from tiny images,

Reference 17

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Observation cfb1e9ea-6e45-48d9-b3ef-2bf5b7224955 · outbound

This paper cites M2m: Imbalanced classification via major-to-minor translation,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection M2m: Imbalanced classification via major-to-minor translation,

Reference 18

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Observation 59430150-e20b-4516-8bac-07f469f9c0ad · outbound

This paper cites Large- scale long-tailed recognition in an open world,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Large- scale long-tailed recognition in an open world,

Reference 19

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Observation 1e2c1245-9164-412a-be9b-dcfff1658482 · outbound

This paper cites ℓ p-norm support vector data description,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection ℓ p-norm support vector data description,

Reference 20

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Observation 1dd274e2-4439-4867-91c2-a3c371309ec5 · outbound

This paper cites One-class classification usingℓ p-norm mul- tiple kernel fisher null approach,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection One-class classification usingℓ p-norm mul- tiple kernel fisher null approach,

Reference 21

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Observation 71b42844-2165-4634-9503-4d78be8d8dc0 · outbound

This paper cites Sta- bilizing adversarially learned one-class novelty detection using pseudo anomalies,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Sta- bilizing adversarially learned one-class novelty detection using pseudo anomalies,

Reference 22

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Observation 2a9bdcbc-f910-472a-99ac-0ef13f7ba0e3 · outbound

This paper cites Target be- fore shooting: Accurate anomaly detection and localization under one millisecond via cascade patch retrieval,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Target be- fore shooting: Accurate anomaly detection and localization under one millisecond via cascade patch retrieval,

Reference 23

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Observation 644d7c4c-db1b-4e80-97db-0a9c85a8d4df · outbound

This paper cites Drocc: Deep robust one-class classification,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Drocc: Deep robust one-class classification,

Reference 24

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Observation 6cbd818d-e343-4b8d-92eb-81cb8f0a23df · outbound

This paper cites Ocmst: One-class novelty detection using convolutional neural network and minimum spanning trees,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Ocmst: One-class novelty detection using convolutional neural network and minimum spanning trees,

Reference 25

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Observation d186bd8c-d9b0-4766-92e0-bdafecb7fc43 · outbound

This paper cites Inter- pretable maximum margin deep anomaly detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Inter- pretable maximum margin deep anomaly detection,

Reference 26

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Observation fd8741a5-5ff5-4dfe-a9db-bb9d9cd4c666 · outbound

This paper cites Csi: Novelty detection via contrastive learning on distributionally shifted instances,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Csi: Novelty detection via contrastive learning on distributionally shifted instances,

Reference 27

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Observation 1308e7e6-d038-4be1-896c-1a9d87c3d6f5 · outbound

This paper cites Deep anomaly de- tection with outlier exposure,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Deep anomaly de- tection with outlier exposure,

Reference 28

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Observation 9deb116f-ea72-4e3b-8624-c473242d758b · outbound

This paper cites Universal novelty detection through adaptive contrastive learning,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Universal novelty detection through adaptive contrastive learning,

Reference 29

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Observation 53145bc8-d77b-4ebd-98ed-7b35e6092644 · outbound

This paper cites Learning in-distribution representations for anomaly detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Learning in-distribution representations for anomaly detection,

Reference 30

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Observation 90e02898-d71d-4292-a00f-e31393cf0c78 · outbound

This paper cites Anomalyclip: Object- agnostic prompt learning for zero-shot anomaly detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Anomalyclip: Object- agnostic prompt learning for zero-shot anomaly detection,

Reference 31

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Observation 6c35710d-7cd1-4014-97c1-24a950f51446 · outbound

This paper cites Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip,

Reference 32

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Observation 61c39c65-d13d-4da2-b330-47fad046a9e6 · outbound

This paper cites Fever-ood: Free energy vulnerability elimination for robust out-of-distribution detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Fever-ood: Free energy vulnerability elimination for robust out-of-distribution detection,

Reference 33

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Observation 10b423bb-fd03-4663-b6bc-17e23a39f515 · outbound

This paper cites Dual energy-based model with open-world un- certainty estimation for out-of-distribution detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Dual energy-based model with open-world un- certainty estimation for out-of-distribution detection,

Reference 34

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Observation 3622cd02-f847-4502-b2e2-dc3096897e79 · outbound

This paper cites Oodd: Test-time out-of-distribution detection with dynamic dictionary,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Oodd: Test-time out-of-distribution detection with dynamic dictionary,

Reference 35

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Observation 23061c09-eec3-4aab-a02b-7c26ce61a503 · outbound

This paper cites Model-free test time adaptation for out-of-distribution detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Model-free test time adaptation for out-of-distribution detection,

Reference 36

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Observation ca31dfff-51b9-44ab-a789-e28fc00ed11b · outbound

This paper cites Decoupling representation and classifier for long-tailed recognition,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Decoupling representation and classifier for long-tailed recognition,

Reference 37

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Observation 47b4eb6e-1376-483e-8a2b-548bb764e7fc · outbound

This paper cites Improving calibration for long-tailed recognition,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Improving calibration for long-tailed recognition,

Reference 38

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Observation 690ab6c0-1966-47ac-a7ef-9cb08253573a · outbound

This paper cites Reslt: Residual learning for long-tailed recognition,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Reslt: Residual learning for long-tailed recognition,

Reference 39

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Observation e00939b0-5432-4136-b60d-7ed8200778eb · outbound

This paper cites Supervised exploratory learning for long-tailed visual recognition,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Supervised exploratory learning for long-tailed visual recognition,

Reference 40

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Observation c5dbcaf7-8337-4185-8a73-0afd377d3940 · outbound

This paper cites Bce3s: Binary cross-entropy based tripartite synergistic learning for long-tailed recognition,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Bce3s: Binary cross-entropy based tripartite synergistic learning for long-tailed recognition,

Reference 41

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Observation 1411aef8-5e56-43d4-9cec-59c55bcdd63e · outbound

This paper cites Out-of-distribution de- tection in long-tailed recognition with calibrated outlier class learning,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Out-of-distribution de- tection in long-tailed recognition with calibrated outlier class learning,

Reference 42

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Observation af5eaf47-7973-4fb3-805a-76201bdd74e4 · outbound

This paper cites Eat: Towards long-tailed out- of-distribution detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Eat: Towards long-tailed out- of-distribution detection,

Reference 43

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Observation edcd393e-3735-4103-bac6-fb57b6cf69fd · outbound

This paper cites Rethinking out-of-distribution detection on imbalanced data distribution,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Rethinking out-of-distribution detection on imbalanced data distribution,

Reference 44

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Observation 12a25b7d-7c72-428d-910a-e577af498f9a · outbound

This paper cites Long-tailed out-of-distribution detection via normalized outlier distribution adaptation,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Long-tailed out-of-distribution detection via normalized outlier distribution adaptation,

Reference 45

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Observation fcbd74bd-1b38-42bd-be8d-b74b1f7b30a1 · outbound

This paper cites Revisiting Frank-Wolfe: Projection-free sparse convex op- timization,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Revisiting Frank-Wolfe: Projection-free sparse convex op- timization,

Reference 46

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Observation d1c3faa7-117d-4be8-855e-5aa0b4f6cbbb · outbound

This paper cites Nesterov,Lectures on Convex Optimization, 2nd ed.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Nesterov,Lectures on Convex Optimization, 2nd ed

Reference 47

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Observation 7b024d3b-bd50-4493-9334-51eca58ce105 · outbound

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

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 48

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Observation 680558ad-21b4-4d30-96e4-b45d80176c95 · outbound

This paper cites On the use of a friedman-type statistic in balanced and unbalanced block designs,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection On the use of a friedman-type statistic in balanced and unbalanced block designs,

Reference 49

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Observation 0e556afc-fccc-4d61-893e-f0a5c0030958 · outbound

This paper cites Deep residual learning for image recognition,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Deep residual learning for image recognition,

Reference 50

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Observation d257435d-4642-4d0c-8b7c-104c796cf5ce · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Emerging properties in self-supervised vision transformers,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:43.744605Z

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Observation 3a166fc2-2268-4dd1-9d4c-62b1ff962a0a · outbound

This paper cites Efficient anomaly detection using self-supervised multi-cue tasks,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Efficient anomaly detection using self-supervised multi-cue tasks,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T05:32:43.824259Z

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Observation deb8d458-fb3a-41a6-9015-5944c65d976f · outbound

This paper cites Learning and evalu- ating representations for deep one-class classification,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Learning and evalu- ating representations for deep one-class classification,

Reference 53

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Observation 44b02893-b83a-43f4-a6eb-99307dffa1b0 · outbound

This paper cites Admm-srnet: Alternating direction method of multipliers based sparse representation network for one-class classification,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Admm-srnet: Alternating direction method of multipliers based sparse representation network for one-class classification,

Reference 54

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Observation 3092d403-155c-4a5f-8261-8a66e367949a · outbound

This paper cites Latent outlier exposure for anomaly detection with contaminated data,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Latent outlier exposure for anomaly detection with contaminated data,

Reference 55

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Observation 27351c8c-b3ad-4da9-97b8-d2d697982807 · outbound

This paper cites Uni- laterally aggregated contrastive learning with hierarchical augmentation for anomaly detection,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Uni- laterally aggregated contrastive learning with hierarchical augmentation for anomaly detection,

Reference 56

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Observation 78ae8ff0-439e-4ea3-84f2-e5bbd487afb1 · outbound

This paper cites RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

Reference 57

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Observation 1cd1128a-e61a-47b8-9e6b-441ac1b82e2c · outbound

This paper cites An evidence-based post-hoc adjustment framework for anomaly detection under data contamination,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection An evidence-based post-hoc adjustment framework for anomaly detection under data contamination,

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Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Self supervision to distillation for long- tailed visual recognition,

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This paper cites Weight balancing for long- tailed image classification,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Weight balancing for long- tailed image classification,

Reference 60

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Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Learning from multiple experts: Self- paced knowledge distillation for long-tailed classification,

Reference 61

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This paper cites Parametric contrastive learning,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Parametric contrastive learning,

Reference 62

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This paper cites Balanced contrastive learning for long-tailed visual recognition,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Balanced contrastive learning for long-tailed visual recognition,

Reference 63

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This paper cites Diffult: Diffusion for long- tail recognition without external knowledge,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Diffult: Diffusion for long- tail recognition without external knowledge,

Reference 64

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This paper cites Ltrl: Boosting long-tail recognition via reflective learning,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Ltrl: Boosting long-tail recognition via reflective learning,

Reference 65

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This paper cites Focal-sam: Focal sharpness-aware minimization for long-tailed classi- fication,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Focal-sam: Focal sharpness-aware minimization for long-tailed classi- fication,

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This paper cites Long-tailed classification with multi-granularity seman- tics,.

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection Long-tailed classification with multi-granularity seman- tics,

Reference 67

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