Paper Citation Record · LEDGER

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation

As of 20 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.20996.

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

pith.paper-citation-record.v1
2507.20996 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:10:30.261122Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0c92335d-6fe3-493f-9abc-c369c5998128 · outbound

This paper cites Deep residual learning for image recognition,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Deep residual learning for image recognition,

Reference 1

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Observation bf2e6e6b-de12-47ed-94c4-8caf8f18ab30 · outbound

This paper cites Fast r-cnn,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Fast r-cnn,

Reference 2

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Observation d661a137-daa8-4084-a363-4147d2b85507 · outbound

This paper cites Deep speech 2: End-to-end speech recognition in english and mandarin,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Deep speech 2: End-to-end speech recognition in english and mandarin,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:33.797037Z

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Observation cbcf82d5-b74e-4f54-a3c5-20c03b6aa174 · outbound

This paper cites Intriguing properties of neural networks.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Intriguing properties of neural networks

Reference 4

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Observation 999014eb-b2fc-4f64-923f-bb0e75458b9c · outbound

This paper cites Explaining and harnessing adversarial examples. proceedings of the 3rd international conference on learning representations, iclr 2015,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Explaining and harnessing adversarial examples. proceedings of the 3rd international conference on learning representations, iclr 2015,

Reference 5

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Observation 623ee1bc-a9f4-418d-bd82-661d9ac5c17b · outbound

This paper cites Countering Adversarial Images using Input Transformations.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Countering Adversarial Images using Input Transformations

Reference 6

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Observation 9bb82667-e3ec-4a70-b4f0-3345bd10a13d · outbound

This paper cites Defense against adversarial attacks using high-level representation guided denoiser,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Defense against adversarial attacks using high-level representation guided denoiser,

Reference 7

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Observation 453766f8-fe24-4fa3-ba2b-8fdad49695e1 · outbound

This paper cites Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Reference 8

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Observation ccab8a8c-06ab-486d-bc64-5aba3c7cdeb8 · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation A simple unified framework for detecting out-of-distribution samples and adversarial attacks,

Reference 9

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Observation 08857bde-cc42-4a76-b957-338c026cc50d · outbound

This paper cites Certified adversarial robustness via randomized smoothing,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Certified adversarial robustness via randomized smoothing,

Reference 10

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Observation d6304759-769d-4bd3-9a58-727bb2d604b1 · outbound

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

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 11

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Observation 5ef3df00-2073-48f3-88c3-d5dca361b35a · outbound

This paper cites Adversarial robustness through local linearization,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Adversarial robustness through local linearization,

Reference 12

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Observation d504dab1-7ac2-4c1d-af52-f13fbf0c49e9 · outbound

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

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Theoretically principled trade-off between robustness and accuracy,

Reference 13

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Observation 1e720ea3-b9d2-44cb-9333-e1b26e3a7cf7 · outbound

This paper cites Improving adversarial robustness requires revisiting misclassified examples,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Improving adversarial robustness requires revisiting misclassified examples,

Reference 14

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Observation 38d36101-7ed7-493c-8d69-f6883a897079 · outbound

This paper cites Magnet: a two-pronged defense against adver- sarial examples,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Magnet: a two-pronged defense against adver- sarial examples,

Reference 15

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Observation 9527b2f0-3468-4fd0-983f-4a9a6802a1b8 · outbound

This paper cites Image super- resolution as a defense against adversarial attacks,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Image super- resolution as a defense against adversarial attacks,

Reference 16

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Observation 7a381a2c-26d8-4f63-b5bd-4c9755d5f4b4 · outbound

This paper cites Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression

Reference 17

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This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,

Reference 18

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Observation 38bada16-cdd9-4a04-ac46-d30730b4b2e3 · outbound

This paper cites Adversarially robust distillation,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Adversarially robust distillation,

Reference 19

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Observation f0391d41-a0ac-4b12-88a7-5e02b437ae8b · outbound

This paper cites Improving Adversarial Robustness via Channel-wise Activation Suppressing.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Improving Adversarial Robustness via Channel-wise Activation Suppressing

Reference 20

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Observation 3869761c-2186-40d2-9a3e-a1a96c4438ab · outbound

This paper cites Robust overfitting may be mitigated by properly learned smoothening,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Robust overfitting may be mitigated by properly learned smoothening,

Reference 21

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Observation 5e11760f-e306-4de7-95cd-a803360e463d · outbound

This paper cites Reliable Adversarial Distillation with Unreliable Teachers.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Reliable Adversarial Distillation with Unreliable Teachers

Reference 22

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This paper cites Ensemble Adversarial Training: Attacks and Defenses.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Ensemble Adversarial Training: Attacks and Defenses

Reference 23

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This paper cites Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training

Reference 24

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Observation 2d1e3443-c13c-449e-9552-d9b7128e02cf · outbound

This paper cites Adversarial attacks and de- fences competition,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Adversarial attacks and de- fences competition,

Reference 25

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Observation 3d87186d-8705-4e5d-825f-695dc7f3f6bc · outbound

This paper cites Deepfool: a simple and accurate method to fool deep neural networks,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Deepfool: a simple and accurate method to fool deep neural networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:32.166639Z

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Observation c815da73-eeff-4321-8811-db39667a48b9 · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Towards evaluating the robustness of neural networks,

Reference 27

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Observation 167a151f-8e46-4507-a41f-0bdd58b2e12b · outbound

This paper cites Adversarial risk and the dangers of evaluating against weak attacks,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Adversarial risk and the dangers of evaluating against weak attacks,

Reference 28

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This paper cites Do wider neural networks really help adversarial robustness?.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Do wider neural networks really help adversarial robustness?

Reference 29

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This paper cites Unlabeled data improves adversarial robustness,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Unlabeled data improves adversarial robustness,

Reference 30

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This paper cites Improving the Generalization of Adversarial Training with Domain Adaptation.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Improving the Generalization of Adversarial Training with Domain Adaptation

Reference 31

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This paper cites Adversarial weight perturbation helps robust generalization,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Adversarial weight perturbation helps robust generalization,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:31.390059Z

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Observation 11a12567-367b-4e34-9323-ad3a729219dd · outbound

This paper cites Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Reference 33

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This paper cites Adversarial training for free!.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Adversarial training for free!

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:31.227864Z

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Observation bb1a9e98-d18f-426c-9489-cf98a08f4cfa · outbound

This paper cites A kernelized manifold mapping to diminish the effect of adversarial perturbations,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation A kernelized manifold mapping to diminish the effect of adversarial perturbations,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:31.074601Z

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Observation 79726bbf-b7b0-4751-8a03-1906ca464abb · outbound

This paper cites Improving adversarial ro- bustness via promoting ensemble diversity,.

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation Improving adversarial ro- bustness via promoting ensemble diversity,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:30.887758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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