Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation | Paper Citation Record

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This paper cites Knowledge distillation: A survey.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Knowledge distillation: A survey

Reference 1

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Observation aba72c8c-dc16-4c74-b3a0-91d51ac3deb0 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Distilling the Knowledge in a Neural Network

Reference 2

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This paper cites Decoupled knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Decoupled knowledge distillation

Reference 3

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This paper cites From knowledge distillation to self- knowledge distillation: A unified approach with normalized loss and customized soft labels.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation From knowledge distillation to self- knowledge distillation: A unified approach with normalized loss and customized soft labels

Reference 4

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This paper cites Knowledge distillation based on transformed teacher matching.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Knowledge distillation based on transformed teacher matching

Reference 5

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This paper cites Exploring inter-channel correlation for diversity-preserved knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Exploring inter-channel correlation for diversity-preserved knowledge distillation

Reference 6

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Observation c3a47f9b-c9f6-448c-af39-3a532efcfb84 · outbound

This paper cites Better teacher better student: Dynamic prior knowledge for knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Better teacher better student: Dynamic prior knowledge for knowledge distillation

Reference 7

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This paper cites Function-consistent feature distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Function-consistent feature distillation

Reference 8

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This paper cites Deep residual learning for im- age recognition.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Deep residual learning for im- age recognition

Reference 9

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Observation 16f5423c-e155-4323-b50f-b76fbf8da08d · outbound

This paper cites The Elements of Statistial Learning.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation The Elements of Statistial Learning

Reference 10

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This paper cites Wasserstein generative adversarial networks.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Wasserstein generative adversarial networks

Reference 11

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Observation 07436eaa-9b39-45eb-9242-77c82b1ebc27 · outbound

This paper cites Abou-Moustafa and Frank P.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Abou-Moustafa and Frank P

Reference 12

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Observation 844301ba-cdd4-4e8f-9939-81676457b4b3 · outbound

This paper cites Wasserstein dependency measure for representation learning.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Wasserstein dependency measure for representation learning

Reference 13

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Observation dbbd8e1b-da28-4d00-8c54-2788ee02f16b · outbound

This paper cites Computational optimal transport: With applications to data science.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Computational optimal transport: With applications to data science

Reference 14

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Observation e49c4aa3-deab-4125-8875-e46a223ef060 · outbound

This paper cites Wasser- stein contrastive representation distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Wasser- stein contrastive representation distillation

Reference 15

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Observation 58c0c442-a277-482f-a2dd-22f7b2e43c02 · outbound

This paper cites Model compression using optimal transport.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Model compression using optimal transport

Reference 16

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Observation 867c7251-4ac0-4445-b13d-56c5d79a26e8 · outbound

This paper cites Similarity of neural network representations revisited.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Similarity of neural network representations revisited

Reference 17

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Observation e7455fcf-28b9-4eb1-9f56-29691da32a18 · outbound

This paper cites Algorithms for learning kernels based on centered alignment.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Algorithms for learning kernels based on centered alignment

Reference 18

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This paper cites Pattern Recognition and Machine Learning.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Pattern Recognition and Machine Learning

Reference 19

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This paper cites Wasserstein Riemannian geometry of Gaussian densities.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Wasserstein Riemannian geometry of Gaussian densities

Reference 20

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Observation dbf34b77-6060-4120-a6c4-0b42ab81005d · outbound

This paper cites Measuring statistical dependence with hilbert-schmidt norms.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Measuring statistical dependence with hilbert-schmidt norms

Reference 21

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Observation ec982d4b-74f1-41d9-aa4e-91813a909924 · outbound

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Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Unresolved cited work

Reference 22

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Observation 1b5cfe3f-4b4b-4f18-a187-eb5f216b23ec · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Sinkhorn distances: Lightspeed computation of optimal transport

Reference 23

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Observation 9233f7fc-fc8e-4b56-9159-e296933b9a8f · outbound

This paper cites Fitnets: Hints for thin deep nets.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Fitnets: Hints for thin deep nets

Reference 24

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Observation ab479a94-6e6b-41ef-9210-1b08cfa650c1 · outbound

This paper cites Contrastive representation distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Contrastive representation distillation

Reference 25

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Observation bf173e6b-eefa-4b47-a0d0-37a4f7febaca · outbound

This paper cites Improved feature distillation via projector ensemble.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Improved feature distillation via projector ensemble

Reference 26

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This paper cites Journal of Multivariate Analysis, 88(2):365–411, 2004.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Journal of Multivariate Analysis, 88(2):365–411, 2004

Reference 27

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Observation 73f13f7d-0fdc-4372-bae3-addf917bc929 · outbound

This paper cites Spatial pyramid pooling in deep convolutional networks for visual recognition.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Spatial pyramid pooling in deep convolutional networks for visual recognition

Reference 28

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Observation a3383a1b-1211-49a7-9e7d-222ec593329e · outbound

This paper cites Distilling knowledge via knowl- edge review.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Distilling knowledge via knowl- edge review

Reference 29

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Observation 669ddbdf-78fa-43af-a2ff-e441266f5d51 · outbound

This paper cites On information and sufficiency.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation On information and sufficiency

Reference 30

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This paper cites An invariant form for the prior probability in estimation problems.Proceedings of the Royal Society of London.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation An invariant form for the prior probability in estimation problems.Proceedings of the Royal Society of London

Reference 31

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Observation 8b739f87-1b2b-43ee-8576-a8fb75cc927d · outbound

This paper cites From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel hilbert space.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel hilbert space

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.196690Z

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This paper cites Deep CNNs meet global covariance pooling: Better representation and generalization.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Deep CNNs meet global covariance pooling: Better representation and generalization

Reference 33

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Observation 56fbbd5f-776c-48a0-b714-d64783db393a · outbound

This paper cites A fast proximal point method for computing exact Wasserstein distance.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation A fast proximal point method for computing exact Wasserstein distance

Reference 34

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Observation f977b480-bd46-4be7-9f37-e28f8798a8ec · outbound

This paper cites Like What You Like: Knowledge Distill via Neuron Selectivity Transfer.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Like What You Like: Knowledge Distill via Neuron Selectivity Transfer

Reference 35

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This paper cites Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer

Reference 36

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This paper cites Knowledge distillation via adaptive instance normalization.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Knowledge distillation via adaptive instance normalization

Reference 37

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This paper cites Positive Definite Matrices.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Positive Definite Matrices

Reference 38

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Observation 6e3fb577-252a-4d8a-be7d-9488e5b3cbf1 · outbound

This paper cites Dimensionality reduction on SPD manifolds: The emergence of geometry-aware methods.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Dimensionality reduction on SPD manifolds: The emergence of geometry-aware methods

Reference 39

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This paper cites Lawrence, and Zhenwen Dai.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Lawrence, and Zhenwen Dai

Reference 40

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Observation d182fcb6-23e9-4d98-9618-c658f5319b32 · outbound

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

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation ImageNet: A large- scale hierarchical image database

Reference 41

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Observation 958a879c-5d5c-404d-9b01-c94e7919c414 · outbound

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

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Learning multiple layers of features from tiny images

Reference 42

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Observation 673c3bd8-20cc-462d-834c-0648c6e4c177 · outbound

This paper cites Microsoft coco: Common objects in context.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Microsoft coco: Common objects in context

Reference 43

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Observation eddf9aa6-7e5d-4160-82c2-736caf4f2f73 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Pytorch: An imperative style, high-performance deep learning library

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.110698Z

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This paper cites Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H

Reference 45

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Observation 0d2080e3-8f95-41e4-9fa1-c5d6f882cf86 · outbound

This paper cites One-for-all: Bridge the gap between heterogeneous architectures in knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation One-for-all: Bridge the gap between heterogeneous architectures in knowledge distillation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.088965Z

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Observation beda3083-0b75-4d6c-bd2e-27dfc8650a3b · outbound

This paper cites Faster R-CNN: Towards real-time object detection with region proposal networks.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Faster R-CNN: Towards real-time object detection with region proposal networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.079514Z

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Observation 7208354e-97a9-45e4-8d0e-23ba0d93f619 · outbound

This paper cites Feature pyramid networks for object detection.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Feature pyramid networks for object detection

Reference 48

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Observation 385a90cd-f267-47aa-89c1-e5ee7fe521d5 · outbound

This paper cites Detectron2.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Detectron2

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.272633Z

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Observation 485c9d8a-f810-4907-bc6b-72d1c72661c1 · outbound

This paper cites Distilling object detectors with fine- grained feature imitation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Distilling object detectors with fine- grained feature imitation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.059334Z

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Observation e9694932-4810-42d6-a4ef-ee9a8161553a · outbound

This paper cites Instance- conditional knowledge distillation for object detection.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Instance- conditional knowledge distillation for object detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.049970Z

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Observation 6a6e7623-2628-4143-bce5-d79259bfe52c · outbound

This paper cites Elementary estimators for sparse covariance matrices and other structured moments.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Elementary estimators for sparse covariance matrices and other structured moments

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.040390Z

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Observation 3da8b5f3-6bbd-4bcb-a184-362d74de197f · outbound

This paper cites On rényi divergence measures for continuous alphabet sources.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation On rényi divergence measures for continuous alphabet sources

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.031610Z

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Observation 65b84277-5176-46a5-9b7f-e410dd83a6b4 · outbound

This paper cites Curriculum temperature for knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Curriculum temperature for knowledge distillation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.021344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5eee052d-7475-4726-a7ae-94dbc5165e6d · outbound

This paper cites Class attention transfer based knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Class attention transfer based knowledge distillation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:41.011658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c4ac3cb1-40ff-45b1-ae09-e4a72aab88bb · outbound

This paper cites Kd-zero: Evolving knowledge distiller for any teacher-student pairs.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Kd-zero: Evolving knowledge distiller for any teacher-student pairs

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.999706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 248a95b6-1bcf-4e7a-b437-ad2b27f1463c · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.306129Z

Source-reported events for the cited work

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Observation f6125a51-2dea-4872-89ac-280bade33136 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.309277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a4ed2db-7bd8-4de2-8917-ffd54e191b1e · outbound

This paper cites A convnet for the 2020s.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation A convnet for the 2020s

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.311946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efa47d41-2185-4d58-b61b-0c1ce25b2a6c · outbound

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

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.314749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee1a5d5f-2085-430a-996f-ebf424cba8a2 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Training data-efficient image transformers & distillation through attention

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.367756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b83ac21-f702-4cd5-a416-7fc1af9cd177 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.458933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3968423a-f50f-4700-b8db-8d2bf8d06fd4 · outbound

This paper cites Knowledge distillation from a stronger teacher.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Knowledge distillation from a stronger teacher

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.851435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 89d4b15c-af3a-4e1f-a886-ef5ebb2d37ea · outbound

This paper cites Correlation congruence for knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Correlation congruence for knowledge distillation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.842497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 119f7532-f51e-412e-a282-c1fa10a7be69 · outbound

This paper cites Relational knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Relational knowledge distillation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.468352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a529735-3f3f-4be4-879a-b16211d89017 · outbound

This paper cites Born again neural networks.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Born again neural networks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.471879Z

Source-reported events for the cited work

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Observation b40782d0-10ce-42ea-bf85-c8fab831c379 · outbound

This paper cites Revisiting knowledge distillation via label smoothing regularization.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Revisiting knowledge distillation via label smoothing regularization

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.823746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 46c8e9fb-e9ba-4900-979c-517f8824d62c · outbound

This paper cites Refine myself by teaching myself: Feature refinement via self-knowledge distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Refine myself by teaching myself: Feature refinement via self-knowledge distillation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.814075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e52d822e-646b-4acc-b7c2-01c39ac7d3dd · outbound

This paper cites Efficient one pass self-distillation with zipf’s label smoothing.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Efficient one pass self-distillation with zipf’s label smoothing

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.802930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 359f7aa4-3e60-4f67-b203-3626e8c3f24c · outbound

This paper cites Near-linear time approximation algorithms for optimal transport via sinkhorn iteration.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Near-linear time approximation algorithms for optimal transport via sinkhorn iteration

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.792855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7adb055f-a372-47b9-a5ed-7f1c9736597b · outbound

This paper cites Masked generative distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Masked generative distillation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.783397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 750a9e92-3fe7-41d0-bedb-d6dc4ee07fc8 · outbound

This paper cites Knowledge diffusion for distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Knowledge diffusion for distillation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.773376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e1e805b7-550e-4553-9f72-970f82d2daee · outbound

This paper cites Multi-level logit distillation.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Multi-level logit distillation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.763874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4b955287-3d19-4057-852a-b33ffae3276d · outbound

This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Randaugment: Practical automated data augmentation with a reduced search space

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.754166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b6d81607-a135-477a-bf35-6dce15074d83 · outbound

This paper cites Revisit the power of vanilla knowledge distillation: from small scale to large scale.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Revisit the power of vanilla knowledge distillation: from small scale to large scale

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.744729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dd8b6cca-8348-4192-bb9a-86c1579fb3b4 · outbound

This paper cites Wide residual networks.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Wide residual networks

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.592910Z

Source-reported events for the cited work

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Observation eefaace2-5ed1-4ea7-8f25-883b9c22da83 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Very deep convolutional networks for large-scale image recognition

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.596707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 618a6903-d25f-4bc5-a6da-5f8fab452134 · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural network for mobile devices.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Shufflenet: An extremely efficient convolutional neural network for mobile devices

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.727571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e79a6529-81c3-459f-9a44-ef80b8b08c2a · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.603731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbfc7846-3246-4077-a6c6-02a95c0868bd · outbound

This paper cites Distance-iou loss: Faster and better learning for bounding box regression.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Distance-iou loss: Faster and better learning for bounding box regression

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.716189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 927c71a4-10c6-460b-bac8-2fd1e07fadbe · outbound

This paper cites Revisiting knowledge distillation via label smoothing regularization.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Revisiting knowledge distillation via label smoothing regularization

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.707734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 84fe2b49-bfc3-4be7-a890-bb56ee1795f5 · outbound

This paper cites Language models are few-shot learners.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Language models are few-shot learners

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.695709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ef97d470-b605-4e0c-bda4-620f5ccc40f6 · outbound

This paper cites GPT-4 Technical Report.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation GPT-4 Technical Report

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-11T18:13:40.616182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87497aee-7062-4edf-a305-9849419847ad · outbound

This paper cites What knowledge gets distilled in knowledge distillation? In Advances in Neural Information Process- ing Systems, 2023.

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation What knowledge gets distilled in knowledge distillation? In Advances in Neural Information Process- ing Systems, 2023

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:13:40.687072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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