Paper Citation Record · LEDGER

Simplifying DINO via Coding Rate Regularization

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 6 inbound Pith citation observations for arXiv:2502.10385.

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

pith.paper-citation-record.v1
2502.10385 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T18:32:14.158944Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:45:29.331367Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:39:16.420475Z

Reference resolution

47 of 47 outbound references displayed

verified exact2
verified fuzzy12
unresolved33
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 09dfed78-ca78-4f02-86ea-46764dfad435 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

Simplifying DINO via Coding Rate Regularization Self-supervised learning from images with a joint-embedding predictive architecture

Reference 1

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Observation 86a6f0d2-234e-43e1-944a-890ebfa24ed0 · outbound

This paper cites Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks.

Simplifying DINO via Coding Rate Regularization Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

Reference 2

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Observation f80e8cf8-a0cb-4de8-81e4-0d33f7f2a1ab · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

Simplifying DINO via Coding Rate Regularization VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 3

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Observation a02c895c-4b09-4675-b0e1-7d108b4e706f · outbound

This paper cites J., Gy \"o rfi, L., Van der Meulen, E.

Simplifying DINO via Coding Rate Regularization J., Gy \"o rfi, L., Van der Meulen, E

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:14.730105Z

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Observation 7080e787-4cf6-42b5-87e4-876c24779ba5 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Simplifying DINO via Coding Rate Regularization D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 5

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Observation 62aa4929-5496-4643-8573-166f628f59b6 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Simplifying DINO via Coding Rate Regularization Unsupervised learning of visual features by contrasting cluster assignments

Reference 6

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Observation f00608ee-8fbf-428f-97b9-9e781c576b92 · outbound

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

Simplifying DINO via Coding Rate Regularization Emerging properties in self-supervised vision transformers

Reference 7

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Observation f1721a16-e09d-4f36-ad9e-fdf1d5cc539e · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Simplifying DINO via Coding Rate Regularization A simple framework for contrastive learning of visual representations

Reference 8

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Observation 06c888b2-4f7a-4fe0-af28-fc48cba395fb · outbound

This paper cites and He, K.

Simplifying DINO via Coding Rate Regularization and He, K

Reference 9

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Observation 5840d80f-afc9-4c63-aada-f2f6df24fa88 · outbound

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

Simplifying DINO via Coding Rate Regularization Sinkhorn distances: Lightspeed computation of optimal transport

Reference 10

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Observation 24502955-cd8b-4f64-8e87-407afd56d4a1 · outbound

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Simplifying DINO via Coding Rate Regularization Unresolved cited work

Reference 11

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Observation 86ea3955-305f-4e1d-a44b-13afcb2d1016 · outbound

This paper cites and Fournier, N.

Simplifying DINO via Coding Rate Regularization and Fournier, N

Reference 12

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Observation c8db3808-b65a-4105-9b00-84744998649f · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Simplifying DINO via Coding Rate Regularization BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 13

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Observation 5ab352de-48fa-4137-8ebb-096f01014fc9 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Simplifying DINO via Coding Rate Regularization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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Observation df7d181e-23ae-4eb2-9a4f-f134d301c114 · outbound

This paper cites Masked autoencoders as spatiotemporal learners.

Simplifying DINO via Coding Rate Regularization Masked autoencoders as spatiotemporal learners

Reference 15

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Observation 02327a77-254f-4bdc-bf17-7ec412978422 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Simplifying DINO via Coding Rate Regularization Bootstrap your own latent-a new approach to self-supervised learning

Reference 16

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Observation ca774501-a91b-4a05-9974-f86d64335ed7 · outbound

This paper cites Dimensionality reduction by learning an invariant mapping.

Simplifying DINO via Coding Rate Regularization Dimensionality reduction by learning an invariant mapping

Reference 17

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Observation 157b005d-fa09-4669-ab15-3117819d21d2 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Simplifying DINO via Coding Rate Regularization Momentum contrast for unsupervised visual representation learning

Reference 18

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Observation 81a54f45-785c-43d3-84fc-adeffc6c7c2e · outbound

This paper cites Masked autoencoders are scalable vision learners.

Simplifying DINO via Coding Rate Regularization Masked autoencoders are scalable vision learners

Reference 19

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Observation 8d46b3ca-eeeb-409a-9657-2f056d330e98 · outbound

This paper cites and Marshall, A.

Simplifying DINO via Coding Rate Regularization and Marshall, A

Reference 20

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Observation d1df81c7-1f7a-4d29-af4f-bdb371246ed6 · outbound

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Simplifying DINO via Coding Rate Regularization Neural Manifold Clustering and Embedding

Reference 21

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Observation 470c664d-ab88-4345-a8d0-04edc173b91f · outbound

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Simplifying DINO via Coding Rate Regularization Unresolved cited work

Reference 22

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Observation 7ca0bbcf-b337-4633-93b6-596cb01fb6bf · outbound

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Simplifying DINO via Coding Rate Regularization Decoupled Weight Decay Regularization

Reference 23

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Observation aac8d634-c002-4ab3-8150-0fadbd12e040 · outbound

This paper cites Segmentation of multivariate mixed data via lossy data coding and compression.

Simplifying DINO via Coding Rate Regularization Segmentation of multivariate mixed data via lossy data coding and compression

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:14.595510Z

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Observation 10f57125-7775-42c5-8741-a63c6d2281bc · outbound

This paper cites Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning.

Simplifying DINO via Coding Rate Regularization Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning

Reference 25

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Observation 83f2fdca-f5a9-420b-8490-80d0d1e4944e · outbound

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Simplifying DINO via Coding Rate Regularization Representation Learning with Contrastive Predictive Coding

Reference 26

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Simplifying DINO via Coding Rate Regularization DINOv2: Learning Robust Visual Features without Supervision

Reference 27

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This paper cites W., Buchanan, S., Yu, Y., and Ma, Y.

Simplifying DINO via Coding Rate Regularization W., Buchanan, S., Yu, Y., and Ma, Y

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:32:14.586357Z

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Observation 0fdd4316-e04b-435c-b320-73f56bb5afc5 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

Simplifying DINO via Coding Rate Regularization The 2017 DAVIS Challenge on Video Object Segmentation

Reference 29

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Observation caef8c6d-1a64-4071-a364-b2342413eef2 · outbound

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Simplifying DINO via Coding Rate Regularization Improving language understanding by generative pre-training

Reference 30

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Simplifying DINO via Coding Rate Regularization Language models are unsupervised multitask learners

Reference 31

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Simplifying DINO via Coding Rate Regularization W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 32

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Simplifying DINO via Coding Rate Regularization and Kingma, D

Reference 33

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Observation 9f1e7eaf-f58e-4296-b92e-115813994c40 · outbound

This paper cites Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations.

Simplifying DINO via Coding Rate Regularization Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations

Reference 34

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Simplifying DINO via Coding Rate Regularization Unsupervised Learning of Structured Representations via Closed-Loop Transcription

Reference 35

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Simplifying DINO via Coding Rate Regularization Training data-efficient image transformers & distillation through attention

Reference 36

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Simplifying DINO via Coding Rate Regularization X., and Misra, I

Reference 37

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This paper cites Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation.

Simplifying DINO via Coding Rate Regularization Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation

Reference 38

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Simplifying DINO via Coding Rate Regularization X., and Lin, D

Reference 39

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Simplifying DINO via Coding Rate Regularization Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction

Reference 40

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Simplifying DINO via Coding Rate Regularization Scaling White-Box Transformers for Vision

Reference 41

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Simplifying DINO via Coding Rate Regularization Learning efficient coding of natural images with maximum manifold capacity representations

Reference 42

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Simplifying DINO via Coding Rate Regularization Unresolved cited work

Reference 43

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Simplifying DINO via Coding Rate Regularization White-box transformers via sparse rate reduction

Reference 44

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Simplifying DINO via Coding Rate Regularization Scene parsing through ade20k dataset

Reference 45

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Simplifying DINO via Coding Rate Regularization iBOT: Image BERT Pre-Training with Online Tokenizer

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Simplifying DINO via Coding Rate Regularization write newline

Reference 47

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Pith citing papers

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M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision cites this paper.

M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision Simplifying DINO via Coding Rate Regularization

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BenchECG and xECG: a benchmark and baseline for ECG foundation models cites this paper.

BenchECG and xECG: a benchmark and baseline for ECG foundation models Simplifying DINO via Coding Rate Regularization

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Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus cites this paper.

Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus Simplifying DINO via Coding Rate Regularization

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Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction cites this paper.

Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction Simplifying DINO via Coding Rate Regularization

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Unsupervised Semantic Segmentation Facilitates Model Understanding cites this paper.

Unsupervised Semantic Segmentation Facilitates Model Understanding Simplifying DINO via Coding Rate Regularization

Reference 30

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Unsupervised Semantic Segmentation Facilitates Model Understanding cites this paper.

Unsupervised Semantic Segmentation Facilitates Model Understanding Simplifying DINO via Coding Rate Regularization

Reference 30

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