Paper Citation Record · LEDGER

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

As of 9 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 2026-08-09T06:31:02.800959+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

No source-named external measurement is stored.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:37.787886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:34.709340Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:37.638178Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:35.896411Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:35.466982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:31.511903Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:31.728385Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:31.959129Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:32.114370Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:32.218757Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:32.346308Z

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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

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:00:34.444186Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:32.659004Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:37.180436Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:32.852011Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:36.928155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:36.557720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:36.415409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:36.206545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:35.749457Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:35.589725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:35.370013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T20:00:35.256272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T20:00:35.135533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:34.782735Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:35.030379Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:34.877996Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:00:37.329757Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:31.310838Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:31.147887Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:31.849871Z

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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

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

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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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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

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

Source-reported events for the cited work

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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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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Unavailable: canonical work link unavailable.

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 2026-08-09T06:31:02.800959+00:00.