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Paper Citation Record · LEDGER

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need

As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2507.04119.

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

pith.paper-citation-record.v1
2507.04119 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:00:34.227773Z

measured 38 of 38 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-17T00:33:29.282349Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T00:33:44.633443Z

Reference resolution

37 of 37 outbound references displayed

  • verified exact1
  • verified fuzzy17
  • unresolved17
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External citation measurements

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Outbound references

Observation 6658231a-7484-44d9-8663-4ba513ef2eb4 · outbound

This paper cites A new backdoor attack in cnns by training set corruption without label poison- ing.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need A new backdoor attack in cnns by training set corruption without label poison- ing

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 4636e154-4c89-4cb6-abdb-d3ea2602e842 · outbound

This paper cites Considering that the adaptive backdoor attack in Qi et al.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Considering that the adaptive backdoor attack in Qi et al

Reference 3

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 744b92d3-7d25-4487-90c4-efc335d10bbc · outbound

This paper cites Sophon: Non-fine-tunable learning to restrain task transferability for pre-trained models.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Sophon: Non-fine-tunable learning to restrain task transferability for pre-trained models

Reference 4

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:00:31.052091Z digest=sha256:f49215cea451cb8ed42dfc1ab14f6b263e032f53a14fe8757cd1ea200fd33ec5

Observation 0153556a-4bad-4d04-b54c-3fcca66212fd · outbound

This paper cites As a result, the generator G is trained to synthesize both ID-like and OOD-like samples (i.e., ID-to-OOD synthetic distribution shift).

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need As a result, the generator G is trained to synthesize both ID-like and OOD-like samples (i.e., ID-to-OOD synthetic distribution shift)

Reference 7

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 0e313427-de59-4f2a-97b7-1a61d480ab66 · outbound

This paper cites We report the ID domain accuracy (IAcc) in blue and OOD domain accuracy (OAcc) in red.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need We report the ID domain accuracy (IAcc) in blue and OOD domain accuracy (OAcc) in red

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:00:33.603370Z digest=sha256:691d810f55481a631247b5af676ed4558e52389ab511b33e9602b130ea30327c

Observation a48d5ec9-690b-4207-90f0-026b89c8519c · outbound

This paper cites Adversarial Attacks on Neural Network Policies.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Adversarial Attacks on Neural Network Policies

Reference 10

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Observation 84c04823-0d09-4757-b405-ab8c7c39fb34 · outbound

This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 12

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source=pdf_text observed=2026-08-06T20:00:31.728385Z digest=sha256:5be15d8a38e78a7759b9ecea20d597582d9a0e34c64ad76e27b7651028680eea

Observation 1e5d9879-8a50-43e8-b3cf-4b1059954f45 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 14

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source=pdf_text observed=2026-08-06T20:00:31.959129Z digest=sha256:47d39cda68ab5f1b07fefc281f2356a12c07da6f5b7a4a2dd12e0ee9644655e1

Observation b46d39f5-e881-4a00-be55-4c7390253c26 · outbound

This paper cites Effects of Degradations on Deep Neural Network Architectures.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Effects of Degradations on Deep Neural Network Architectures

Reference 15

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Observation 86a50ab5-4b4e-424b-8899-774b9d865bcf · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 16

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source=pdf_text observed=2026-08-06T20:00:32.218757Z digest=sha256:931cece16b920508bd2eae73ae9440d905cf987ab698730adbe3022beaf94b7e

Observation 8c7b3f91-ed62-4482-bd2a-ef5c86f5c9bf · outbound

This paper cites Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models

Reference 17

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source=pdf_text observed=2026-08-06T20:00:32.346308Z digest=sha256:820d39af747f9cabf865c962b641f14ce48b41531306bb269d7397f2339e7c05

Observation 96c6cb24-3691-47d5-b9d9-0fe9718c3f81 · outbound

This paper cites Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising

Reference 18

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 21f788bc-250a-4d3d-97ce-f67fbeb52b7f · outbound

This paper cites Representation Surgery for Multi-Task Model Merging.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Representation Surgery for Multi-Task Model Merging

Reference 20

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Observation 24b3defc-a1f2-466c-b914-6410cfd6150b · outbound

This paper cites and Lu, W.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need and Lu, W

Reference 21

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e8bc6e07-5d24-4db1-95ee-73c5da743162 · outbound

This paper cites Understanding the Interaction of Adversarial Training with Noisy Labels.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Understanding the Interaction of Adversarial Training with Noisy Labels

Reference 22

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source=pdf_text observed=2026-08-06T20:00:32.852011Z digest=sha256:7e5bb0945d378618d879965cec250879a4ba91a6f207bab38cf0d858fd7984b6

Observation d05fbe05-b891-4fd8-8512-1146544ec527 · outbound

This paper cites The former one faces efficiency issues due to per-image optimization, and the latter one needs extra data.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need The former one faces efficiency issues due to per-image optimization, and the latter one needs extra data

Reference 23

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source=pdf_text observed=2026-08-06T20:00:32.986442Z digest=sha256:55b49e137508ab71f968a76f2605586ac2c02ce00d7f8186eef175cbcdbbbf1d

Observation 2104e7a8-feae-4eec-9a05-9c65ee757229 · outbound

This paper cites However, only using adversarial exploration can bring non-stationary distribution problem and loss the diversity of synthesizing.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need However, only using adversarial exploration can bring non-stationary distribution problem and loss the diversity of synthesizing

Reference 24

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Observation 7b2b2a6a-dd9e-4520-8a12-719993240419 · outbound

This paper cites This indicates that these samples are more similar to real OOD samples and can activate NTL teacher’s OOD misleading knowledge.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need This indicates that these samples are more similar to real OOD samples and can activate NTL teacher’s OOD misleading knowledge

Reference 25

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source=pdf_text observed=2026-08-06T20:00:33.184135Z digest=sha256:0498f0854b3bdbac031d9ecc88b9ce36c16e639b74c3eab921123fd71561bff3

Observation 065daacf-b498-4ae8-b436-e12071844d8c · outbound

This paper cites In DFKD, SOTA methods such as Choi et al.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need In DFKD, SOTA methods such as Choi et al

Reference 26

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Observation 91e1bb2b-06e7-4738-bd8c-91cfac5d9fb5 · outbound

This paper cites The NTL teacher is trained on CIFAR10→STL10 with VGG-13.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need The NTL teacher is trained on CIFAR10→STL10 with VGG-13

Reference 28

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source=pdf_text observed=2026-08-06T20:00:33.447501Z digest=sha256:5fa05db44660b5b929496eb4ea5bf91a5a428030a623e7b03c2639dca00ca3e6

Observation 9dffea96-9fb3-4918-b179-aae7ef77c177 · outbound

This paper cites This is because learning correct classification results in relatively complex decision boundaries between classes and small margins6 for ID domain data points.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need This is because learning correct classification results in relatively complex decision boundaries between classes and small margins6 for ID domain data points

Reference 29

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source=pdf_text observed=2026-08-06T20:00:33.520985Z digest=sha256:d1f42dc923eaf76a577528cd6fcfbf1421c087310771e3e65c2193fe0f8b746b

Observation f7ce3689-cf1f-4d8a-995d-4f26e0b51b2b · outbound

This paper cites We follow the implementation of DFQ and CMI in https://github.com/zju-vipa/CMI and NAYER in https://github.com/tmtuan1307/NAYER.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need We follow the implementation of DFQ and CMI in https://github.com/zju-vipa/CMI and NAYER in https://github.com/tmtuan1307/NAYER

Reference 31

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source=pdf_text observed=2026-08-06T20:00:33.676648Z digest=sha256:3671f0e6994418b78bdf0d366c5e470f7ba9d4c55ef2378de895f1b0992db965

Observation 5ed6cace-b0be-467c-be72-95204311eb6f · outbound

This paper cites We report the ID domain accuracy in blue and OOD domain accuracy in red.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need We report the ID domain accuracy in blue and OOD domain accuracy in red

Reference 32

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source=pdf_text observed=2026-08-06T20:00:33.755106Z digest=sha256:dcbe3d19ef8d60c7578704808de6ebbb5c5e88de20c0b4e04d696561c69dbef5

Observation 94eadf70-977c-4e1e-8205-cc04ccaa1c43 · outbound

This paper cites We report the ID domain accuracy in blue and OOD domain accuracy in red.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need We report the ID domain accuracy in blue and OOD domain accuracy in red

Reference 33

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source=pdf_text observed=2026-08-06T20:00:33.881424Z digest=sha256:92ccd121703b25c4025209941c8d9b3effaff70d97b10ddffca7023ff0de3397

Observation 29de162b-d6e7-4c72-8e8f-4d9da3885c29 · outbound

This paper cites Our work explores the DFKD under NTL teachers.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Our work explores the DFKD under NTL teachers

Reference 36

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source=pdf_text observed=2026-08-06T20:00:34.117024Z digest=sha256:b162650d51a27ac4cbdc12d47be51d3dba2a40c26982ba76ae7aef356b197300

Observation 4af70630-7a1d-49b4-bfc4-994eeafdd0db · outbound

This paper cites T : ResNet34 and S: ResNet18).

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need T : ResNet34 and S: ResNet18)

Reference 49

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:00:33.927936Z digest=sha256:03bd1605424cdf333002fa68ca3fe562d3e42ea5548c8dd1b3288f01dcdca1c2

Observation 8dcf104c-730b-4301-b7a0-f1fc40f1f04e · outbound

This paper cites Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

Reference 2011

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source=pdf_text observed=2026-08-06T20:00:30.892874Z digest=sha256:e7e67bdb5e59cc6538b98a6a419e71b1adc43a1a4cfe71b84fc78ad37619724f

Observation f2febe44-5e89-4da1-bc04-4d825bb8b7de · outbound

This paper cites • Wang et al.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need • Wang et al

Reference 2013

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source=pdf_text observed=2026-08-06T20:00:34.029439Z digest=sha256:7dc444afa8d985c1fcccc76c2237145487213353418a7ff3ab8f88f310344c6f

Observation 74581569-715d-4c0a-83a7-0db7b5f1b513 · outbound

This paper cites Domain-adversarial training of neural networks.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Domain-adversarial training of neural networks

Reference 2015

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:00:31.224979Z digest=sha256:3c1960bb24b5b4280b2ef92568629b51bb8bd6ef1b92a28d853cfbe456d6a87e

Observation b5df5915-9f13-4073-9226-d929199f0d5f · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Explaining and Harnessing Adversarial Examples

Reference 2016

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source=pdf_text observed=2026-08-06T20:00:31.310838Z digest=sha256:a06a70c55e722ab0760bee9d2182815cd9490a015ce056cccf035ae8e3fc12dd

Observation c3ff255b-6cb1-4c27-983b-590c8795fbb8 · outbound

This paper cites Contrastive Model Inversion for Data-Free Knowledge Distillation.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Contrastive Model Inversion for Data-Free Knowledge Distillation

Reference 2019

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source=pdf_text observed=2026-08-06T20:00:31.147887Z digest=sha256:7bf0f5e69256b44e352bdd51a3fd3178f65a42191e5fffe216c6ffd5d5a292eb

Observation 21fe55f3-f4a4-46f7-a9b4-800a83cbd45c · outbound

This paper cites Data-Free Knowledge Transfer: A Survey.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Data-Free Knowledge Transfer: A Survey

Reference 2020

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:31.849871Z digest=sha256:6a0ec2e2820fe6a72731243a347eb9a8a0f4ae12dd4c23aea37d3807f509988b

Observation b06e6d5e-54b5-4a6c-b173-eeec80e3e370 · outbound

This paper cites Auto-Encoding Variational Bayes.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Auto-Encoding Variational Bayes

Reference 2021

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unresolved
no resolver link, observed 2026-08-06T20:00:31.611448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:31.611448Z digest=sha256:b4665cf9a8b2169b8d01a6a9763b3f4de9f9b99b3440c1052c7cfb5871d769e4

Observation 30541b2d-ef85-43d4-915e-a75cb4ec8f3e · outbound

This paper cites Data-Free Adversarial Distillation.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Data-Free Adversarial Distillation

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-06T20:00:31.095392Z

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source=pdf_text observed=2026-08-06T20:00:31.095392Z digest=sha256:24dd0620ea632a4e4c226c9c36cb419badad37dc0b7ddd0a2961f93aca15f516

Observation 64e58dc1-7c50-4788-becc-b53d26d8b490 · outbound

This paper cites Toward Robust Non-Transferable Learning: A Survey and Benchmark.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Toward Robust Non-Transferable Learning: A Survey and Benchmark

Reference 2023

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unresolved
no resolver link, observed 2026-08-06T20:00:31.411267Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:31.411267Z digest=sha256:f76fbb8dd77c64b05168c882d56a6a5318be7abed0de7fe7c2e527ee1ea9985e

Observation 50191fd6-36a9-405c-ad07-83cdaf7fd09d · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 2024

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unresolved
no resolver link, observed 2026-08-06T20:00:30.973647Z

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source=pdf_text observed=2026-08-06T20:00:30.973647Z digest=sha256:adaf51ee27301578cd649ff96e8f2726ae7d319059645ce0ec63cfbd3bf5d4b6

Observation d6101d65-332e-4357-a97a-9933907f3cc2 · outbound

This paper cites Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness.

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness

Reference 2025

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unresolved
no resolver link, observed 2026-08-06T20:00:32.560579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:32.560579Z digest=sha256:69c3b1f740a7e4b8102000963a8a3ea60ef0304bc3193d1504ab9ee2b90c291f

Pith citing papers

Observation a0efd0cc-75c7-47af-9011-d3247c44d2dc · inbound

RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing cites this paper.

RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-17T00:33:44.637012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-17T00:33:29.282349Z digest=sha256:d58877155ec55d1f5c0b119f8f4509d4702d3590fe8f0b0a1f00a4ec2d24dd5f