Improving Out-of-Distribution Detection via Dynamic Covariance Calibration | Paper Citation Record

Observation ca5d1e09-42f6-4656-97a0-b6c57aa6dabd · outbound

This paper cites H., Park, G.-M., and Kim, S.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration H., Park, G.-M., and Kim, S

Reference 1

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This paper cites B., Belkhir, N., Popescu, S., Manzanera, A., and Franchi, G.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration B., Belkhir, N., Popescu, S., Manzanera, A., and Franchi, G

Reference 2

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Scaling out-of-distribution detection for real-world settings

Reference 3

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This paper cites L., Li, X., He, W., Gou, L., and Ren, L.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration L., Li, X., He, W., Gou, L., and Ren, L

Reference 4

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Emerging properties in self-supervised vision transformers

Reference 5

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This paper cites Atom: Robustifying out-of-distribution detection using outlier mining.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Atom: Robustifying out-of-distribution detection using outlier mining

Reference 6

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Unresolved cited work

Reference 7

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This paper cites Describing textures in the wild.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Describing textures in the wild

Reference 8

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Observation 9ed1b652-331a-4b0a-abd8-dca2f2fcbbcd · outbound

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Imagenet: A large-scale hierarchical image database

Reference 9

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Learning Confidence for Out-of-Distribution Detection in Neural Networks

Reference 10

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

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This paper cites Grood: Gradient-aware out-of-distribution detection in interpolated manifolds.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Grood: Gradient-aware out-of-distribution detection in interpolated manifolds

Reference 12

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Observation 5bacf770-39cf-4328-a019-940d42d448a8 · outbound

This paper cites Can autonomous vehicles identify, recover from, and adapt to distribution shifts? In International Conference on Machine Learning, pp.\ 3145--3153.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Can autonomous vehicles identify, recover from, and adapt to distribution shifts? In International Conference on Machine Learning, pp.\ 3145--3153

Reference 13

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This paper cites Evidence reconciled neural network for out-of-distribution detection in medical images.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Evidence reconciled neural network for out-of-distribution detection in medical images

Reference 14

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Unresolved cited work

Reference 15

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This paper cites A survey on vision transformer.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration A survey on vision transformer

Reference 16

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This paper cites Deep residual learning for image recognition.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Deep residual learning for image recognition

Reference 17

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This paper cites Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem

Reference 18

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This paper cites and Gimpel, K.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration and Gimpel, K

Reference 19

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This paper cites Natural adversarial examples.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Natural adversarial examples

Reference 20

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This paper cites Pixmix: Dreamlike pictures comprehensively improve safety measures.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Pixmix: Dreamlike pictures comprehensively improve safety measures

Reference 21

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Observation 25405d7e-7afa-44f7-a8b6-c57d4f5b321d · outbound

This paper cites and Li, Y.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration and Li, Y

Reference 22

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This paper cites On the importance of gradients for detecting distributional shifts in the wild.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration On the importance of gradients for detecting distributional shifts in the wild

Reference 23

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This paper cites Enhancing camera surveillance using computer vision: a research note.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Enhancing camera surveillance using computer vision: a research note

Reference 24

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This paper cites Openimages: A public dataset for large-scale multi-label and multi-class image classification.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Openimages: A public dataset for large-scale multi-label and multi-class image classification

Reference 25

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This paper cites Crafting papers on machine learning.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Crafting papers on machine learning

Reference 26

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 27

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This paper cites Enhancing the reliability of out-of-distribution image detection in neural networks.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Enhancing the reliability of out-of-distribution image detection in neural networks

Reference 28

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This paper cites Fast Decision Boundary based Out-of-Distribution Detector.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Fast Decision Boundary based Out-of-Distribution Detector

Reference 29

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Energy-based out-of-distribution detection

Reference 30

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration and Li, Y

Reference 31

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This paper cites Y., et al.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Y., et al

Reference 32

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This paper cites Boundary-aware backward-compatible representation via adversarial learning in image retrieval.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Boundary-aware backward-compatible representation via adversarial learning in image retrieval

Reference 33

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Unresolved cited work

Reference 34

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This paper cites Conjnorm: Tractable density estimation for out-of-distribution detection.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Conjnorm: Tractable density estimation for out-of-distribution detection

Reference 35

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This paper cites I., and Strapasson, J.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration I., and Strapasson, J

Reference 36

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection

Reference 37

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration \ SSD \ : A unified framework for self-supervised outlier detection

Reference 38

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This paper cites and Li, Y.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration and Li, Y

Reference 39

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration React: Out-of-distribution detection with rectified activations

Reference 40

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Out-of-distribution detection with deep nearest neighbors

Reference 41

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets

Reference 42

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Training data-efficient image transformers & distillation through attention

Reference 43

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This paper cites The inaturalist species classification and detection dataset.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration The inaturalist species classification and detection dataset

Reference 44

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Unresolved cited work

Reference 45

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Vim: Out-of-distribution with virtual-logit matching

Reference 46

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This paper cites Energy-based open-world uncertainty modeling for confidence calibration.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Energy-based open-world uncertainty modeling for confidence calibration

Reference 47

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This paper cites A., Oliva, A., and Torralba, A.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration A., Oliva, A., and Torralba, A

Reference 48

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This paper cites Scaling for training time and post-hoc out-of-distribution detection enhancement.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Scaling for training time and post-hoc out-of-distribution detection enhancement

Reference 49

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking

Reference 50

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This paper cites Openood: Benchmarking generalized out-of-distribution detection.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Openood: Benchmarking generalized out-of-distribution detection

Reference 51

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This paper cites Generalized out-of-distribution detection: A survey.

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Generalized out-of-distribution detection: A survey

Reference 52

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 53

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

Improving Out-of-Distribution Detection via Dynamic Covariance Calibration Places: A 10 million image database for scene recognition

Reference 54

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Improving Out-of-Distribution Detection via Dynamic Covariance Calibration write newline

Reference 55

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