Paper Citation Record · LEDGER

Non-Linear Outlier Synthesis for Out-of-Distribution Detection

As of 13 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2411.13619.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.13619 v1

Coverage vector

measured 58 of 58 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T00:29:11.066026Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T16:29:57.959480Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation f193d2cc-6ec6-4dba-804b-dbb1b34233d9 · outbound

This paper cites Latent space autoregression for novelty detec- tion.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Latent space autoregression for novelty detec- tion

Reference 1

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Observation d19fd995-fcaa-4e02-9895-9f6a342d3061 · outbound

This paper cites Detecting semantic anomalies.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Detecting semantic anomalies

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.973868Z

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Observation 1b1c90e9-f288-431b-973b-48aae1c39c18 · outbound

This paper cites Gradorth: A simple yet efficient out- of-distribution detection with orthogonal projection of gra- dients.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Gradorth: A simple yet efficient out- of-distribution detection with orthogonal projection of gra- dients

Reference 3

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Observation ef72ea1f-68c6-474e-86d4-0eda081cd378 · outbound

This paper cites Fodfom: Fake outlier data by founda- tion models creates stronger visual out-of-distribution detec- tor.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Fodfom: Fake outlier data by founda- tion models creates stronger visual out-of-distribution detec- tor

Reference 4

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Observation 7734a19f-aedc-46ac-92f4-a1904a22fdd7 · outbound

This paper cites Describing textures in the wild.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Describing textures in the wild

Reference 5

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Observation 2598b77d-b597-4000-ba54-9b7ec5ba0869 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Imagenet: A large-scale hierarchical image database

Reference 6

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Observation 34df565c-5ffd-492a-819f-7525552337b3 · outbound

This paper cites Learning Confidence for Out-of-Distribution Detection in Neural Networks.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Learning Confidence for Out-of-Distribution Detection in Neural Networks

Reference 7

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Observation 16d5e4d8-979b-4ac7-add4-1df90afb212e · outbound

This paper cites Diffusion models beat gans on image synthesis.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Diffusion models beat gans on image synthesis

Reference 8

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Observation 96c99662-87f3-422c-8959-a4e29f5f743e · outbound

This paper cites Data invariants to understand unsupervised out-of- distribution detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Data invariants to understand unsupervised out-of- distribution detection

Reference 9

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Observation cff3cea3-2065-4f35-9788-2f928ea89ece · outbound

This paper cites Learning non-linear invariants for unsupervised out- of-distribution detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Learning non-linear invariants for unsupervised out- of-distribution detection

Reference 10

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Observation 4798d850-a736-4e03-80be-8a687d532b0c · outbound

This paper cites V os: Learning what you don’t know by virtual outlier synthesis.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection V os: Learning what you don’t know by virtual outlier synthesis

Reference 11

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Observation a50fd070-da19-4667-9097-2bb243cc5866 · outbound

This paper cites Dream the impossible: Outlier imagination with diffusion models.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Dream the impossible: Outlier imagination with diffusion models

Reference 12

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Observation 7e04ec82-6da4-4dda-9358-dc4f5b5331d5 · outbound

This paper cites Ex- ploring the limits of out-of-distribution detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Ex- ploring the limits of out-of-distribution detection

Reference 13

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Observation 40cb9d44-8be4-46ae-ba12-a045c0216a9f · outbound

This paper cites Transfusion–a transparency-based diffusion model for anomaly detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Transfusion–a transparency-based diffusion model for anomaly detection

Reference 14

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Observation 39e920dc-ca8f-4e65-92f9-2c8ba5a7d096 · outbound

This paper cites An image is worth one word: Personalizing text-to-image gen- eration using textual inversion.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection An image is worth one word: Personalizing text-to-image gen- eration using textual inversion

Reference 15

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Observation f58eea9e-369c-4294-9230-a7c5d1e44ece · outbound

This paper cites Hierarchical vaes know what they don’t know.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Hierarchical vaes know what they don’t know

Reference 16

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Observation f5697bec-a8fa-48cd-be0d-e39b1ca5d49e · outbound

This paper cites A baseline for detect- ing misclassified and out-of-distribution examples in neural networks.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection A baseline for detect- ing misclassified and out-of-distribution examples in neural networks

Reference 17

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Observation e9d4b0fa-cd45-48ef-986b-9fe5eb3b8388 · outbound

This paper cites Deep anomaly detection with outlier exposure.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Deep anomaly detection with outlier exposure

Reference 18

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Observation f91d29a5-322b-4bc9-af40-fdecf8144588 · outbound

This paper cites Using self-supervised learning can improve model robustness and uncertainty.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Using self-supervised learning can improve model robustness and uncertainty

Reference 19

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Observation a1fff900-746b-4981-b382-b4af5d3673ea · outbound

This paper cites Scal- ing out-of-distribution detection for real-world settings.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Scal- ing out-of-distribution detection for real-world settings

Reference 20

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This paper cites Generalized odin: Detecting out-of-distribution image with- out learning from out-of-distribution data.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Generalized odin: Detecting out-of-distribution image with- out learning from out-of-distribution data

Reference 21

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Observation 2c2f4cf1-f07e-4f93-919a-5d05b82db196 · outbound

This paper cites Mos: Towards scaling out-of- distribution detection for large semantic space.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Mos: Towards scaling out-of- distribution detection for large semantic space

Reference 22

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Observation a7529142-ae31-46cf-b0be-5b12d0796634 · outbound

This paper cites On the impor- tance of gradients for detecting distributional shifts in the wild.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection On the impor- tance of gradients for detecting distributional shifts in the wild

Reference 23

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Observation c8f45deb-2fb1-41df-b61e-9149e3023496 · outbound

This paper cites Why is the Mahalanobis Distance Effective for Anomaly Detection?.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Why is the Mahalanobis Distance Effective for Anomaly Detection?

Reference 24

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Observation ebaeb67e-c9fc-42d4-9b63-8a9a57caac8a · outbound

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

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Learning multiple layers of features from tiny images

Reference 25

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Observation bc3bfbf1-4dc2-4833-aa7c-c6cf9d690231 · outbound

This paper cites Training confidence-calibrated classifiers for detecting out- of-distribution samples.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Training confidence-calibrated classifiers for detecting out- of-distribution samples

Reference 26

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Observation 9ff38574-c495-46f2-96ec-e70dbaa8c2b5 · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 27

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Observation ac5c38ac-06df-4e74-888e-22f733c80843 · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly de- tection and localization.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Cutpaste: Self-supervised learning for anomaly de- tection and localization

Reference 28

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Observation be4bbe12-7401-4af1-81bf-57bc98ae9d1a · outbound

This paper cites Enhanc- ing the reliability of out-of-distribution image detection in 9 neural networks.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Enhanc- ing the reliability of out-of-distribution image detection in 9 neural networks

Reference 29

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Observation f5fab3d8-f880-4006-957d-678a16491a5c · outbound

This paper cites Energy-based out-of-distribution detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Energy-based out-of-distribution detection

Reference 30

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Observation 6f7db462-42b2-488b-b6ad-25eee4ee59a3 · outbound

This paper cites Gen: Pushing the limits of softmax-based out-of-distribution de- tection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Gen: Pushing the limits of softmax-based out-of-distribution de- tection

Reference 31

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Observation b29080b0-97ed-4486-88f5-0dfee7364e26 · outbound

This paper cites Do Deep Generative Models Know What They Don't Know?.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Do Deep Generative Models Know What They Don't Know?

Reference 32

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Observation 2fbd41c6-632d-45d7-90c1-bcf1a415889e · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Reading digits in natural images with unsupervised feature learning

Reference 33

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Observation 27f529f8-3ae1-4b1f-9125-9a0260a0ac75 · outbound

This paper cites Outlier exposure with confidence control for out-of-distribution detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Outlier exposure with confidence control for out-of-distribution detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.628835Z

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Observation 61d87244-6f94-4464-9585-e4a6c33b67ee · outbound

This paper cites Mean-shifted contrastive loss for anomaly detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Mean-shifted contrastive loss for anomaly detection

Reference 35

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Observation 686c1674-e1f3-49ee-af80-de667932b9bc · outbound

This paper cites A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection

Reference 36

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Observation 95e79ff8-42b7-43f8-878a-53fd465615e1 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models, 2021.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection High-resolution image syn- thesis with latent diffusion models, 2021

Reference 37

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Observation f50f3867-584b-4169-8871-fa6bd1855205 · outbound

This paper cites Detecting out-of-distribution examples with gram matrices.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Detecting out-of-distribution examples with gram matrices

Reference 38

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Observation edf33a1b-5dfb-4fdc-bae9-08abbb7cb7b1 · outbound

This paper cites Understanding anomaly detection with deep invert- ible networks through hierarchies of distributions and fea- tures.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Understanding anomaly detection with deep invert- ible networks through hierarchies of distributions and fea- tures

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.581768Z

Source-reported events for the cited work

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Observation bbeed7ed-9ebf-4efe-a38f-5194c7cd3911 · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection and localization.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Natural synthetic anomalies for self-supervised anomaly detection and localization

Reference 40

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Observation 65b29c36-2589-4c32-bbb8-d79313db092f · outbound

This paper cites Ssd: A unified framework for self-supervised outlier detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Ssd: A unified framework for self-supervised outlier detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.562800Z

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Observation e5ac22cc-f950-408f-b71e-be96b7182e55 · outbound

This paper cites Input complexity and out-of- distribution detection with likelihood-based generative mod- els.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Input complexity and out-of- distribution detection with likelihood-based generative mod- els

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.551838Z

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Observation a6b772be-6080-4be5-ad4e-c5570fc5f448 · outbound

This paper cites Dice: Leveraging sparsification for out-of-distribution detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Dice: Leveraging sparsification for out-of-distribution detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.540871Z

Source-reported events for the cited work

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Observation a3637d57-980e-4356-9c87-80d1f49ae7e7 · outbound

This paper cites React: Out-of- distribution detection with rectified activations.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection React: Out-of- distribution detection with rectified activations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.529832Z

Source-reported events for the cited work

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Observation 1106850d-8241-4088-9c6d-6f6f82799133 · outbound

This paper cites Out-of- distribution detection with deep nearest neighbors.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Out-of- distribution detection with deep nearest neighbors

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.518309Z

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Observation 35be1739-47f9-4bc2-ae91-193a7eae9a14 · outbound

This paper cites Detecting outliers with poisson image interpolation.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Detecting outliers with poisson image interpolation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.505725Z

Source-reported events for the cited work

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Observation 7e770fcb-dc72-404c-a439-7e073aa63e68 · outbound

This paper cites Non- parametric outlier synthesis.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Non- parametric outlier synthesis

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.494415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9b858d6d-a174-45cc-884e-af5980907ce4 · outbound

This paper cites Self-guided generation of minority samples using diffusion models.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Self-guided generation of minority samples using diffusion models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.481807Z

Source-reported events for the cited work

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Observation 6c42d902-0f90-47ce-b60a-0cb24fa46cc4 · outbound

This paper cites The inaturalist species classification and de- tection dataset.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection The inaturalist species classification and de- tection dataset

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.470283Z

Source-reported events for the cited work

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Observation 76bcc2f7-57f8-4e05-81e4-f3f399c6abe5 · outbound

This paper cites Vim: Out-of-distribution with virtual-logit matching.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Vim: Out-of-distribution with virtual-logit matching

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:51:53.458476Z

Source-reported events for the cited work

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Observation eea58561-310d-4ea6-ad6a-e3332f3139f1 · outbound

This paper cites Contrastive Training for Improved Out-of-Distribution Detection.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Contrastive Training for Improved Out-of-Distribution Detection

Reference 51

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Observation c7ae899d-54f6-49c3-8fac-7bd59a39ab34 · outbound

This paper cites Datasetdm: Synthesizing data with perception annota- tions using diffusion models.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Datasetdm: Synthesizing data with perception annota- tions using diffusion models

Reference 52

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Observation dd1fcbdb-e0ef-4210-a0e0-f463444b2f12 · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Sun database: Large-scale scene recognition from abbey to zoo

Reference 53

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Observation d260d4e2-9e61-4753-8311-d774a2a7635d · outbound

This paper cites Do we really need to learn representations from in-domain data for outlier de- tection? ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning, 2021.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Do we really need to learn representations from in-domain data for outlier de- tection? ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning, 2021

Reference 54

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Observation ec38aa4e-459e-4f5b-be80-f0df32f4cdb9 · outbound

This paper cites TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking

Reference 55

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This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 56

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This paper cites Places: A 10 million image database for scene recognition.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection Places: A 10 million image database for scene recognition

Reference 57

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Observation d7443d71-ef14-427d-be93-a181a0a22493 · outbound

This paper cites The ID datasets are CIFAR-100 and ImageNet-100, which we briefly describe below: CIFAR-100 [25] contains 50’000 training images and 10’000 testing images belonging to 100 classes.

Non-Linear Outlier Synthesis for Out-of-Distribution Detection The ID datasets are CIFAR-100 and ImageNet-100, which we briefly describe below: CIFAR-100 [25] contains 50’000 training images and 10’000 testing images belonging to 100 classes

Reference 58

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Pith citing papers

Observation acf58db5-079a-4b62-9cda-9ba352e3fadb · inbound

Modality-Aware Out-of-Distribution Detection for Multi-Modal Action Recognition cites this paper.

Modality-Aware Out-of-Distribution Detection for Multi-Modal Action Recognition Non-Linear Outlier Synthesis for Out-of-Distribution Detection

Reference 11

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