Cross-Architecture Distillation Made Simple with Redundancy Suppression | Paper Citation Record

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This paper cites Towards a theory of early visual processing.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Towards a theory of early visual processing

Reference 1

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Observation 2b6b0e33-46db-4739-96b0-d4829258e7df · outbound

This paper cites Vi- creg: Variance-invariance-covariance regularization for self- supervised learning.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Vi- creg: Variance-invariance-covariance regularization for self- supervised learning

Reference 2

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Observation 6df12eac-e1a5-490e-8035-240e64de970a · outbound

This paper cites Unsupervised learning.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Unsupervised learning

Reference 3

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Observation fbeb652e-2ec7-423b-98cd-d4da00ebaf0b · outbound

This paper cites Cross-layer distillation with semantic calibration.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Cross-layer distillation with semantic calibration

Reference 4

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Observation ab2bdd44-e9db-4be7-a891-6ffdf8fe2908 · outbound

This paper cites Knowledge distillation with the reused teacher classifier.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Knowledge distillation with the reused teacher classifier

Reference 5

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Observation 1c25bdc2-a162-4542-b8eb-bd6717eee007 · outbound

This paper cites Distilling knowledge via knowledge review.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Distilling knowledge via knowledge review

Reference 6

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Observation da1b7bda-be32-496a-b769-63d8ffdf3000 · outbound

This paper cites Dearkd: data-efficient early knowledge distillation for vision transformers.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Dearkd: data-efficient early knowledge distillation for vision transformers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.254093Z

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Observation b9a9ccb3-fe7f-4554-88bc-5407ba96bf0b · outbound

This paper cites Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.236401Z

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Observation ef5b966b-cab9-4965-a78b-0ff47fc13290 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Xception: Deep learning with depthwise separable convolutions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.219771Z

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Observation df1ab837-4c9d-4fd7-be33-b012fca95fd2 · outbound

This paper cites Kd-dlgan: Data limited image generation via knowledge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Kd-dlgan: Data limited image generation via knowledge distillation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.204101Z

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Observation 98719da1-3d02-46b1-9b9d-254611ad3262 · outbound

This paper cites Non-linear feature extraction by redundancy reduction in an unsupervised stochastic neural network.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Non-linear feature extraction by redundancy reduction in an unsupervised stochastic neural network

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.188793Z

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Observation d7cb1e5c-e6f1-4c21-bbc3-20dc95f463ef · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression Imagenet: A large-scale hierarchical image database

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.174282Z

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Observation ff566120-1a03-45c0-b2ab-a88a382234c8 · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression An image is worth 16x16 words: Transformers for image recognition at scale

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.157046Z

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Observation abf219e8-37d3-48ff-8e0d-4e1dcd52ca80 · outbound

This paper cites Convit: Improving vision transformers with soft convolutional inductive biases.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Convit: Improving vision transformers with soft convolutional inductive biases

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.138493Z

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Observation 6277bf07-17d4-46c8-93b6-9bcac73249fd · outbound

This paper cites Scalekd: Strong vision transformers could be excellent teachers.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Scalekd: Strong vision transformers could be excellent teachers

Reference 15

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Observation 12d8ebeb-95fd-4a53-9381-3ab03c377126 · outbound

This paper cites Domain-adversarial training of neural networks.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Domain-adversarial training of neural networks

Reference 16

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Observation 512c6e4c-d942-4694-9995-75a761be18b8 · outbound

This paper cites On the duality between contrastive and non-contrastive self-supervised learning.

Cross-Architecture Distillation Made Simple with Redundancy Suppression On the duality between contrastive and non-contrastive self-supervised learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.090304Z

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Observation d19a919f-e626-4d23-a11c-646b7366f0fc · outbound

This paper cites Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.072700Z

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Observation c7e1f958-4526-43c7-bc23-1286b687ec41 · outbound

This paper cites Cmt: Convolutional neural networks meet vision transformers.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Cmt: Convolutional neural networks meet vision transformers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.053985Z

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Observation 25e66ed2-cf0f-4dfb-b022-727588fb8dea · outbound

This paper cites Class attention transfer based knowledge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Class attention transfer based knowledge distillation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.030257Z

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Observation 32ccf79c-4b17-494d-bb6e-1be6e348ac4e · outbound

This paper cites Learning effi- cient vision transformers via fine-grained manifold distilla- tion.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Learning effi- cient vision transformers via fine-grained manifold distilla- tion

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:04.010204Z

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Observation 8305ae84-ff3f-4c07-a3d8-ebbd64d88fbb · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression One-for-all: Bridge the gap be- tween heterogeneous architectures in knowledge distillation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.990061Z

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Observation 744b385f-5ef5-4aaf-80d2-7d022072c581 · outbound

This paper cites Deep residual learning for image recognition.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Deep residual learning for image recognition

Reference 23

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Observation 7d20219d-f6cf-4a4a-a57a-d2a160f05482 · outbound

This paper cites A comprehensive overhaul of feature distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression A comprehensive overhaul of feature distillation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.960100Z

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Observation cb4f3009-5e57-4866-bd4d-4719aab031b2 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Distilling the Knowledge in a Neural Network

Reference 25

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Observation a6ed85c2-e811-470e-8c06-69a0c26002f6 · outbound

This paper cites Knowledge distillation from a stronger teacher.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Knowledge distillation from a stronger teacher

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.942639Z

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Observation 2b4347bc-71ec-4d0a-a85e-fb9595755d91 · outbound

This paper cites Evaluation-oriented knowledge distillation for deep face recognition.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Evaluation-oriented knowledge distillation for deep face recognition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.925784Z

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Observation 09f057c9-76e3-40ec-9e2e-3a9283d1d425 · outbound

This paper cites Similarity of neural network representa- tions revisited.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Similarity of neural network representa- tions revisited

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.909683Z

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Observation 68d95008-c78c-4816-93b8-3b1d8042b14b · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression Learning multiple layers of features from tiny images

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.892528Z

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Observation 3cc5f21d-b511-4a0b-909a-8ec7f7ebd7b8 · outbound

This paper cites Scconv: Spatial and channel reconstruction convolution for feature redundancy.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Scconv: Spatial and channel reconstruction convolution for feature redundancy

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.872286Z

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Observation 9449dc09-ab2d-4e55-b5ca-e547e7716a9c · outbound

This paper cites Locality guidance for improving vision trans- formers on tiny datasets.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Locality guidance for improving vision trans- formers on tiny datasets

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.851515Z

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Observation 01142205-42b2-48f6-b609-a190cb29ef6a · outbound

This paper cites Curriculum tempera- ture for knowledge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Curriculum tempera- ture for knowledge distillation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.833631Z

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Observation d0b2b581-8512-4051-926b-1a53453d2a80 · outbound

This paper cites Knowledge distil- lation via the target-aware transformer.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Knowledge distil- lation via the target-aware transformer

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.814244Z

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Observation 80e03f38-c6f6-4105-b4bd-bf320c48c932 · outbound

This paper cites Function-consistent feature distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Function-consistent feature distillation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.796164Z

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Observation 4c5e1370-6c2d-42f7-8ec7-0cefaec7e9f0 · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression Exploring inter-channel correlation for diversity-preserved knowledge distillation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.777989Z

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Observation bb8fdbe5-d147-4d92-b1ad-4000d41c9c72 · outbound

This paper cites Cross-architecture knowledge distilla- tion.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Cross-architecture knowledge distilla- tion

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.746029Z

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Observation 33234d2b-9b34-49f2-bd47-fe01757da601 · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression Swin transformer: Hierarchical vision transformer using shifted windows

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.725509Z

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Observation acd17b93-07ad-451b-960f-59026a6d889d · outbound

This paper cites A convnet for the 2020s.

Cross-Architecture Distillation Made Simple with Redundancy Suppression A convnet for the 2020s

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.708857Z

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Observation 957ed8dc-87d3-4588-95d7-581be087bdc8 · outbound

This paper cites Domain-invariant feature exploration for domain generalization.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Domain-invariant feature exploration for domain generalization

Reference 39

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Observation faa6c3ca-a7d6-43ab-a0d8-c6d8c0d7eefa · outbound

This paper cites Im- proved knowledge distillation via teacher assistant.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Im- proved knowledge distillation via teacher assistant

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.672926Z

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Observation 0d51f6ab-5114-4701-b0c7-00c8b388b378 · outbound

This paper cites Domain generalization via invariant feature representation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Domain generalization via invariant feature representation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.653781Z

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Observation 72b35327-106a-441d-8ca3-7a046aedef44 · outbound

This paper cites Good teachers explain: Explanation- enhanced knowledge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Good teachers explain: Explanation- enhanced knowledge distillation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.636118Z

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Observation 43a84374-b788-49c0-94e8-bc758421cd0c · outbound

This paper cites How do vision transformers work? ICLR, 2022.

Cross-Architecture Distillation Made Simple with Redundancy Suppression How do vision transformers work? ICLR, 2022

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.616658Z

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Observation 190ac115-53e3-49a7-b3bc-6d16fbd65312 · outbound

This paper cites Rela- tional knowledge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Rela- tional knowledge distillation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.594802Z

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Observation 96298d00-102d-4cef-a5f4-fb100c33a928 · outbound

This paper cites Correla- tion congruence for knowledge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Correla- tion congruence for knowledge distillation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.574534Z

Source-reported events for the cited work

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Observation 7c36a22d-dede-424d-b819-af1d3a327bbe · outbound

This paper cites Ef- ficient domain generalization via common-specific low-rank decomposition.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Ef- ficient domain generalization via common-specific low-rank decomposition

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.554530Z

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Observation 9711e5f3-5f89-4fe4-837f-4e6054b25956 · outbound

This paper cites Slimconv: Reducing channel redundancy in convolutional neural networks by features recombining.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Slimconv: Reducing channel redundancy in convolutional neural networks by features recombining

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.535673Z

Source-reported events for the cited work

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Observation 45ef8378-e048-4c54-91c4-5c97ba7c1486 · outbound

This paper cites Do vision trans- formers see like convolutional neural networks? NeurIPS,.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Do vision trans- formers see like convolutional neural networks? NeurIPS,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.518137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a12d87f4-ce60-4d0d-98f9-7d44843c1e00 · outbound

This paper cites Fitnets: Hints for thin deep nets.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Fitnets: Hints for thin deep nets

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.498367Z

Source-reported events for the cited work

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Observation 18ae90cd-fe12-43e5-b226-c94805995b33 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.480834Z

Source-reported events for the cited work

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Observation 78b8cc30-4e44-42c8-ae19-a1693490ab9b · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression Very deep convo- lutional networks for large-scale image recognition

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T12:25:02.608818Z

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Observation 266dcdfe-ffa3-4bda-a0ed-afc383f6aa8d · outbound

This paper cites Logit standardization in knowl- edge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Logit standardization in knowl- edge distillation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.447122Z

Source-reported events for the cited work

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Observation 9a12b5b9-eabe-4a1b-bb10-e1530e2333ed · outbound

This paper cites Con- trastive representation distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Con- trastive representation distillation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.429380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe925098-e8f8-4c2b-b75b-a771687efe37 · outbound

This paper cites Deep learning and the information bottleneck principle.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Deep learning and the information bottleneck principle

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.407474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc089534-414b-49b7-bec7-28a724529fd8 · outbound

This paper cites The information bottleneck method.

Cross-Architecture Distillation Made Simple with Redundancy Suppression The information bottleneck method

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T12:25:02.628772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f362d407-c6b2-4ba4-b0e6-3df860a2ce4a · outbound

This paper cites Mlp-mixer: An all-mlp architecture for vision.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Mlp-mixer: An all-mlp architecture for vision

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.383226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 829a72ed-c619-415f-9263-c9408de63c0c · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression Training data-efficient image transformers & distillation through at- tention

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.361575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation daeb313d-6691-45c3-b404-3997985cd2d7 · outbound

This paper cites Resmlp: Feedforward networks for image classification with data-efficient training.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Resmlp: Feedforward networks for image classification with data-efficient training

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.343357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f3e05aa-00b4-48d8-a195-74545e5378f9 · outbound

This paper cites Similarity-preserving knowl- edge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Similarity-preserving knowl- edge distillation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.322743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 478413f6-4650-42c5-83c9-8155d84bec70 · outbound

This paper cites Attention is all you need.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Attention is all you need

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.299600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1859697a-a622-4a00-8e33-0d79b7b0c3bc · outbound

This paper cites Diffuse and Disperse: Image Generation with Representation Regularization.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Diffuse and Disperse: Image Generation with Representation Regularization

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T12:25:02.661144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51065f5c-11a7-492c-acdb-8716975d930e · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression Dis- tilling object detectors with fine-grained feature imitation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.280860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 33d19d0a-56c6-4e56-9121-ae974a477810 · outbound

This paper cites Glance and focus: a dynamic ap- proach to reducing spatial redundancy in image classifica- tion.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Glance and focus: a dynamic ap- proach to reducing spatial redundancy in image classifica- tion

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.260100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7dbebffd-d61e-47d9-abbb-59bc780dac41 · outbound

This paper cites Improving knowledge distilla- tion via regularizing feature norm and direction.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Improving knowledge distilla- tion via regularizing feature norm and direction

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.237791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0735eee4-f998-4ff7-a8e1-33c233a9d969 · outbound

This paper cites Scale decoupled distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Scale decoupled distillation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.220479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 52127107-cd68-484f-a19b-01d8d2db63a7 · outbound

This paper cites Tinyvit: Fast pretraining distillation for small vision transformers.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Tinyvit: Fast pretraining distillation for small vision transformers

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.201681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation db52777d-9bf8-4a51-87c7-467ccbee877f · outbound

This paper cites High-fidelity 3d gan inversion by pseudo- multi-view optimization.

Cross-Architecture Distillation Made Simple with Redundancy Suppression High-fidelity 3d gan inversion by pseudo- multi-view optimization

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.181817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b537b62-6ec4-4f46-8b27-fbd1f27d3536 · outbound

This paper cites Cross-image relational knowl- edge distillation for semantic segmentation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Cross-image relational knowl- edge distillation for semantic segmentation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.163390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8b554b0-ede6-4011-a21f-3ef14c8f41a2 · outbound

This paper cites Focal and global knowledge distillation for detectors.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Focal and global knowledge distillation for detectors

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.131412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c0b80ef-92ec-4e73-a60b-e37d0a778950 · outbound

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

Cross-Architecture Distillation Made Simple with Redundancy Suppression From knowledge distillation to self- knowledge distillation: A unified approach with normalized loss and customized soft labels

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.111519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b49e1a88-7689-48f6-8cab-3b79af2de733 · outbound

This paper cites Vitkd: Feature-based knowledge distillation for vision transformers.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Vitkd: Feature-based knowledge distillation for vision transformers

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.088441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a2093ad9-9ca3-411d-9952-4780ffab82a1 · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Barlow twins: Self-supervised learning via redundancy reduction

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.070698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5fb41726-23f5-4db0-bdb8-bdbb117fa759 · outbound

This paper cites Foreground object search by distilling composite image feature.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Foreground object search by distilling composite image feature

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.049086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 47246a3c-bacf-4a5c-8154-44964d649c3f · outbound

This paper cites Cross-view consistency regularisation for knowledge distil- lation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Cross-view consistency regularisation for knowledge distil- lation

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.028728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e3ea79d1-9974-493a-86fb-77d8e0b093bf · outbound

This paper cites Alleviating foreground sparsity for semi-supervised monoc- ular 3d object detection.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Alleviating foreground sparsity for semi-supervised monoc- ular 3d object detection

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:03.009178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f00c4e36-6405-4b52-a80c-e06aa6ffc853 · outbound

This paper cites VRM: Knowledge Distillation via Virtual Relation Matching.

Cross-Architecture Distillation Made Simple with Redundancy Suppression VRM: Knowledge Distillation via Virtual Relation Matching

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:25:02.819970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 83722659-a988-4cea-bec4-6b2c197d8031 · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural net- work for mobile devices.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Shufflenet: An extremely efficient convolutional neural net- work for mobile devices

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:02.990273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d7df1b9f-c90c-4add-9aef-f7624ed83d76 · outbound

This paper cites Decoupled knowledge distillation.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Decoupled knowledge distillation

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:02.972390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6b301fb8-ca4f-412b-8aa6-34919bda8be5 · outbound

This paper cites Knowledge distillation based on transformed teaching matching.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Knowledge distillation based on transformed teaching matching

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:02.951862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ef3b18d9-0f5c-400e-b441-9071a26c72d5 · outbound

This paper cites Unidistill: A universal cross-modality knowl- edge distillation framework for 3d object detection in bird’s- eye view.

Cross-Architecture Distillation Made Simple with Redundancy Suppression Unidistill: A universal cross-modality knowl- edge distillation framework for 3d object detection in bird’s- eye view

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:25:02.925358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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