Paper Citation Record · LEDGER

Robust Representation Consistency Model via Contrastive Denoising

As of 11 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2501.13094.

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

pith.paper-citation-record.v1
2501.13094 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:32:20.864488Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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verified fuzzy16
unresolved27
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External citation measurements

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Outbound references

Observation 8178ac95-f733-43c4-a078-9c70202473da · outbound

This paper cites All are worth words: A VIT backbone for diffusion models.

Robust Representation Consistency Model via Contrastive Denoising All are worth words: A VIT backbone for diffusion models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:32:21.445470Z

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Observation ec5e9827-a07e-469a-9255-116d798f93a1 · outbound

This paper cites (Certified!!) Adversarial Robustness for Free!.

Robust Representation Consistency Model via Contrastive Denoising (Certified!!) Adversarial Robustness for Free!

Reference 2

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Observation 051c574f-672f-4adf-b18a-b68e419eedde · outbound

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

Robust Representation Consistency Model via Contrastive Denoising A simple framework for contrastive learning of visual representations

Reference 3

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Observation a37a747c-558d-41ae-bfe4-566e28518ada · outbound

This paper cites Exploring simple siamese representation learning.

Robust Representation Consistency Model via Contrastive Denoising Exploring simple siamese representation learning

Reference 4

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Observation 7f528794-cbba-4029-974b-0ca0fcf6247a · outbound

This paper cites An empirical study of training self-supervised vision transformers.

Robust Representation Consistency Model via Contrastive Denoising An empirical study of training self-supervised vision transformers

Reference 5

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Observation 182bc572-ba33-43d5-a259-da66c61f9c32 · outbound

This paper cites Certified adversarial robustness via randomized smoothing.

Robust Representation Consistency Model via Contrastive Denoising Certified adversarial robustness via randomized smoothing

Reference 6

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Observation 27cf4aad-3844-4353-91e9-ed2350350210 · outbound

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

Robust Representation Consistency Model via Contrastive Denoising Imagenet: A large-scale hierarchical image database

Reference 7

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Observation f9f41044-ed81-4f7e-a2f4-38b5f0acdf07 · outbound

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

Robust Representation Consistency Model via Contrastive Denoising An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation 868caaac-900a-4027-b317-d13524b430bd · outbound

This paper cites Tweedie’s formula and selection bias.

Robust Representation Consistency Model via Contrastive Denoising Tweedie’s formula and selection bias

Reference 9

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Observation 89ded01e-d100-4700-a5ab-50a2cd37828d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Robust Representation Consistency Model via Contrastive Denoising Explaining and Harnessing Adversarial Examples

Reference 10

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Observation 16937cdb-6378-48f5-954e-c5c3ef87fe03 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Robust Representation Consistency Model via Contrastive Denoising Momentum contrast for unsupervised visual representation learning

Reference 11

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Observation 9fadcb4e-c8d5-47c6-ae44-0250686cf63b · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Robust Representation Consistency Model via Contrastive Denoising Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 12

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Observation 2087403e-82c4-4b01-bc29-b1d5fac9820a · outbound

This paper cites Denoising diffusion probabilistic models.

Robust Representation Consistency Model via Contrastive Denoising Denoising diffusion probabilistic models

Reference 13

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Observation 0a01fc42-d0ca-48d0-8198-19da4d283bb9 · outbound

This paper cites Boosting Randomized Smoothing with Variance Reduced Classifiers.

Robust Representation Consistency Model via Contrastive Denoising Boosting Randomized Smoothing with Variance Reduced Classifiers

Reference 14

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Observation e55ac013-264c-4d1c-83be-cd647a6ce0c5 · outbound

This paper cites Consistency regularization for certified robustness of smoothed classifiers.

Robust Representation Consistency Model via Contrastive Denoising Consistency regularization for certified robustness of smoothed classifiers

Reference 15

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Observation f43206d7-7dc5-4360-9248-0feba555342d · outbound

This paper cites Multi-scale diffusion denoised smoothing.

Robust Representation Consistency Model via Contrastive Denoising Multi-scale diffusion denoised smoothing

Reference 16

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Observation e1457026-0c91-4f04-8238-d3779ffbc5b7 · outbound

This paper cites Smoothmix: Training confidence-calibrated smoothed classifiers for certified robustness.

Robust Representation Consistency Model via Contrastive Denoising Smoothmix: Training confidence-calibrated smoothed classifiers for certified robustness

Reference 17

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Observation bfd79d95-c548-4f07-b2b8-4487894af8cc · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Robust Representation Consistency Model via Contrastive Denoising Elucidating the design space of diffusion-based generative models

Reference 18

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Observation d1f14211-14a1-4696-a2af-d71f997fb100 · outbound

This paper cites Auto-Encoding Variational Bayes.

Robust Representation Consistency Model via Contrastive Denoising Auto-Encoding Variational Bayes

Reference 19

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Observation 5eecea03-12aa-46d3-9d93-8cab36b7b476 · outbound

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

Robust Representation Consistency Model via Contrastive Denoising Learning multiple layers of features from tiny images

Reference 20

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Observation c16d6f47-57cb-419d-9be6-750ff07d9613 · outbound

This paper cites Certified robustness to adversarial examples with differential privacy.

Robust Representation Consistency Model via Contrastive Denoising Certified robustness to adversarial examples with differential privacy

Reference 21

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Observation a212d29c-0bb0-4a34-b13c-544c47c18411 · outbound

This paper cites Return of Unconditional Generation: A Self-supervised Representation Generation Method.

Robust Representation Consistency Model via Contrastive Denoising Return of Unconditional Generation: A Self-supervised Representation Generation Method

Reference 22

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Observation 3c07976f-cc0d-4b0a-9029-ad1d18a0b789 · outbound

This paper cites Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness.

Robust Representation Consistency Model via Contrastive Denoising Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness

Reference 23

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Observation 7f86a31f-aa1b-486f-a755-cbfc4ee44108 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

Robust Representation Consistency Model via Contrastive Denoising Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 24

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Observation cdbfe4ac-5d38-4781-b876-9dd449e2cc05 · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Robust Representation Consistency Model via Contrastive Denoising Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 25

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Observation 53410a42-19a7-4a2c-aba8-22ab39f6d1fb · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Robust Representation Consistency Model via Contrastive Denoising Towards deep learning models resistant to adversarial attacks

Reference 26

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Observation 5059a5a2-111b-4156-b335-68b553c61e6e · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Robust Representation Consistency Model via Contrastive Denoising Representation Learning with Contrastive Predictive Coding

Reference 27

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Observation c1eaec99-b880-4a1a-90f3-f45257c613b3 · outbound

This paper cites Certified Defenses against Adversarial Examples.

Robust Representation Consistency Model via Contrastive Denoising Certified Defenses against Adversarial Examples

Reference 28

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Observation cc3de79b-6483-411d-8a3d-034f4aaf5eac · outbound

This paper cites Semidefinite relaxations for certifying robustness to adversarial examples.

Robust Representation Consistency Model via Contrastive Denoising Semidefinite relaxations for certifying robustness to adversarial examples

Reference 29

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Observation 49b2efb4-2306-489e-868a-a35b9987921d · outbound

This paper cites Provably robust deep learning via adversarially trained smoothed classifiers.

Robust Representation Consistency Model via Contrastive Denoising Provably robust deep learning via adversarially trained smoothed classifiers

Reference 30

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Observation 53763289-f28d-4fcc-a72e-7f16a9b6684b · outbound

This paper cites A convex relaxation barrier to tight robustness verification of neural networks.

Robust Representation Consistency Model via Contrastive Denoising A convex relaxation barrier to tight robustness verification of neural networks

Reference 31

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Observation eadd89c7-3874-458a-9bed-7479918f0832 · outbound

This paper cites Denoised smoothing: A provable defense for pretrained classifiers.

Robust Representation Consistency Model via Contrastive Denoising Denoised smoothing: A provable defense for pretrained classifiers

Reference 32

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Observation e26f448b-c0de-4bc6-ad21-b44ba8201528 · outbound

This paper cites Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.

Robust Representation Consistency Model via Contrastive Denoising Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Reference 33

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This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Robust Representation Consistency Model via Contrastive Denoising Score-Based Generative Modeling through Stochastic Differential Equations

Reference 34

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Observation 71f75583-81bd-42c1-bad0-b9dd5399676a · outbound

This paper cites Consistency Models.

Robust Representation Consistency Model via Contrastive Denoising Consistency Models

Reference 35

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Observation 6e516ff2-b68c-4e9e-a4f4-ecab1988a7fd · outbound

This paper cites DensePure: Understanding Diffusion Models towards Adversarial Robustness.

Robust Representation Consistency Model via Contrastive Denoising DensePure: Understanding Diffusion Models towards Adversarial Robustness

Reference 36

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Observation 5a04b0ef-e9f6-44f8-9e2d-c6215510f6a2 · outbound

This paper cites MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius.

Robust Representation Consistency Model via Contrastive Denoising MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius

Reference 37

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Observation 9d884366-f643-4bb6-8cf1-dc6e01abf1f0 · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy.

Robust Representation Consistency Model via Contrastive Denoising Theoretically principled trade-off between robustness and accuracy

Reference 38

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This paper cites Efficient neural network robustness certification with general activation functions.

Robust Representation Consistency Model via Contrastive Denoising Efficient neural network robustness certification with general activation functions

Reference 39

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Observation 775f7a65-4dd6-4883-a899-23ad8a0851b6 · outbound

This paper cites DiffSmooth : Certifiably robust learning via diffusion models and local smoothing.

Robust Representation Consistency Model via Contrastive Denoising DiffSmooth : Certifiably robust learning via diffusion models and local smoothing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:32:21.156414Z

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Observation 1c4551eb-f78c-4ad6-b5fa-00ae05604c49 · outbound

This paper cites write newline.

Robust Representation Consistency Model via Contrastive Denoising write newline

Reference 41

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This paper cites @esa (Ref.

Robust Representation Consistency Model via Contrastive Denoising @esa (Ref

Reference 42

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Robust Representation Consistency Model via Contrastive Denoising Unresolved cited work

Reference 43

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This paper cites The gray gray lines denote the PF ODE trajectories.

Robust Representation Consistency Model via Contrastive Denoising The gray gray lines denote the PF ODE trajectories

Reference 44

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