SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense | Paper Citation Record

Observation 08525fe0-1097-463c-a450-aa3d5a2a9ccd · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Memory aware synapses: Learning what (not) to forget

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.733868Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c324ca86-4347-4706-842b-fb0487cfe903 · outbound

This paper cites Obfus- cated gradients give a false sense of security: Circumventing defenses to adversarial examples.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Obfus- cated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.726008Z

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Observation 926b2aaf-b716-483a-a2de-b6e17000f496 · outbound

This paper cites Training Ensembles to Detect Adversarial Examples.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Training Ensembles to Detect Adversarial Examples

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:22:42.455797Z

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Observation 9b7c3c39-e3ff-486e-a495-f7c94d80142b · outbound

This paper cites Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.067938Z

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Observation e5e6094a-e87a-4e58-bc77-0c4cf6fc5291 · outbound

This paper cites Dokania, and Philip H.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Dokania, and Philip H

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.717533Z

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Observation 96a9a960-f2fa-4ca3-bfb7-3709228c6412 · outbound

This paper cites Zoo: Zeroth order optimization based black- box attacks to deep neural networks without training substi- tute models.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Zoo: Zeroth order optimization based black- box attacks to deep neural networks without training substi- tute models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.709835Z

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Observation 74a09cd8-80a4-46a5-b140-cd373202d9a8 · outbound

This paper cites Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks, 2020.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks, 2020

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.701404Z

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Observation cac313c1-b89a-4381-9343-5cc3e7376d09 · outbound

This paper cites A continual learning survey: Defying for- getting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense A continual learning survey: Defying for- getting in classification tasks.IEEE transactions on pattern analysis and machine intelligence, 44(7):3366–3385, 2021

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.693415Z

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Observation 311135f0-e274-4aac-912e-ce14aa500a7b · outbound

This paper cites Boosting adversarial at- tacks with momentum.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Boosting adversarial at- tacks with momentum

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.685911Z

Source-reported events for the cited work

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Observation f22ee6a1-b149-491b-b845-267c467f798c · outbound

This paper cites Evading defenses to transferable adversarial examples by translation-invariant attacks.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Evading defenses to transferable adversarial examples by translation-invariant attacks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.678450Z

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Observation 7b203e5e-0238-414f-831b-e60702155b64 · outbound

This paper cites Adversarially robust distillation.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Adversarially robust distillation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.669648Z

Source-reported events for the cited work

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Observation 03457ade-6296-4fa6-af84-b19a898ffd0d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Explaining and Harnessing Adversarial Examples

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.090529Z

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Observation 7da81055-82df-4a7c-a6ab-dc37be377f9e · outbound

This paper cites On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.093557Z

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Observation a2a95645-2236-4d25-bdfb-1e24867000e9 · outbound

This paper cites HyperNetworks.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense HyperNetworks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.097206Z

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Observation ca3fc630-4385-4eac-a686-67857bcdb48e · outbound

This paper cites Memory efficient experience replay for streaming learning.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Memory efficient experience replay for streaming learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.660858Z

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Observation 8fda10c0-b958-432b-960f-4bc68afce046 · outbound

This paper cites Deep residual learning for image recognition.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Deep residual learning for image recognition

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.103073Z

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Observation be8a52a5-01e5-4aa1-afb0-02b9bb44ebf0 · outbound

This paper cites Poste- rior meta-replay for continual learning.Advances in Neural Information Processing Systems, 34:14135–14149, 2021.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Poste- rior meta-replay for continual learning.Advances in Neural Information Processing Systems, 34:14135–14149, 2021

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.647845Z

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Observation df788f8a-b2e1-4874-9c35-3151d6a3bbd2 · outbound

This paper cites Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.109043Z

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Observation 1629803a-421f-46de-bab6-343984c116a7 · outbound

This paper cites Adversarial machine learning.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Adversarial machine learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.639225Z

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Observation 0fd91b9f-cd10-4270-b764-3b24ff0d7343 · outbound

This paper cites Las-at: adversarial training with learn- able attack strategy.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Las-at: adversarial training with learn- able attack strategy

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.631950Z

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Observation 567b90fe-2c8d-4fec-82e3-2c54f70f836f · outbound

This paper cites Ape-gan: Adversarial perturbation elimination with gan.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Ape-gan: Adversarial perturbation elimination with gan

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.624478Z

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Observation 4e34046f-239f-490e-b5d3-de1b8dd741f7 · outbound

This paper cites Susceptibility of Continual Learning Against Adversarial Attacks.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Susceptibility of Continual Learning Against Adversarial Attacks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.120512Z

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Observation edf88237-afc9-4c39-8a55-eb618ed7afcb · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the national academy of sci- ences, 114(13):3521–3526, 2017.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the national academy of sci- ences, 114(13):3521–3526, 2017

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.123862Z

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Observation d8caca29-704e-48ed-9e21-89ae398199f8 · outbound

This paper cites HINT: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense HINT: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:22:42.412275Z

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Observation 135484c7-d9f8-445f-a718-21403240b267 · outbound

This paper cites HyperMask: Adaptive Hypernetwork-based Masks for Continual Learning.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense HyperMask: Adaptive Hypernetwork-based Masks for Continual Learning

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:22:42.402269Z

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Observation 5bbd1fed-b8ed-4e26-9c57-d4379ae8d0b0 · outbound

This paper cites Learning without forgetting.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Learning without forgetting

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.132624Z

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Observation cae52e0f-ad91-4a71-be8d-4be5788a4968 · outbound

This paper cites Continual learning with recursive gradient optimization, 2022.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Continual learning with recursive gradient optimization, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.606709Z

Source-reported events for the cited work

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Observation 0ef939a7-cd24-4b7b-8bc6-c64960fc9a14 · outbound

This paper cites Core50: a new dataset and benchmark for continuous object recognition.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Core50: a new dataset and benchmark for continuous object recognition

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.598403Z

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Observation 4a46ef78-3736-49bd-b094-c9737d4f785d · outbound

This paper cites Gradient episodic memory for continual learning.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Gradient episodic memory for continual learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.140347Z

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Observation 83aafc8d-27bb-415c-9d34-4f6ef0c22768 · outbound

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

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.145936Z

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Observation 57221246-d199-45d2-b1e3-a412f9394761 · outbound

This paper cites Packnet: Adding mul- tiple tasks to a single network by iterative pruning.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Packnet: Adding mul- tiple tasks to a single network by iterative pruning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.578809Z

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Observation 8dc29aa5-6dc8-4af6-8841-78cf501e9910 · outbound

This paper cites Target layer regularization for continual learning using cramer-wold distance.Information Sciences, 609:1369–1380, 2022.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Target layer regularization for continual learning using cramer-wold distance.Information Sciences, 609:1369–1380, 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.570345Z

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Observation 16065307-7c38-4765-967b-91f5b513a283 · outbound

This paper cites Catastrophic inter- ference in connectionist networks: The sequential learning problem.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Catastrophic inter- ference in connectionist networks: The sequential learning problem

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.561818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation abecd697-8786-4b92-9819-5a1fa9a0dd4e · outbound

This paper cites Dif- ferentiable abstract interpretation for provably robust neural networks.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Dif- ferentiable abstract interpretation for provably robust neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.554465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de7419b2-5bba-41fe-941c-e2bc391f55e1 · outbound

This paper cites Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:22:42.386867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 980ac240-ac54-47fd-9d1e-d8ca3c932a91 · outbound

This paper cites Deeply supervised discriminative learning for adversarial defense.IEEE trans- actions on pattern analysis and machine intelligence, 43(9): 3154–3166, 2020.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Deeply supervised discriminative learning for adversarial defense.IEEE trans- actions on pattern analysis and machine intelligence, 43(9): 3154–3166, 2020

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.546934Z

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Observation 1242f9ab-bb41-4489-a2ce-04fd587cd602 · outbound

This paper cites Springer, 1993.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Springer, 1993

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.539202Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3e8d38e7-3620-4f5d-a7f1-2ce96d182e00 · outbound

This paper cites Bag of Tricks for Adversarial Training.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Bag of Tricks for Adversarial Training

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.168878Z

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Observation 5168fb23-152c-47f3-b789-3f8e24089457 · outbound

This paper cites Maintaining Adversarial Robustness in Continuous Learning.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Maintaining Adversarial Robustness in Continuous Learning

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:22:42.371045Z

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Observation 80494096-fee4-4ae1-a551-4756009233c0 · outbound

This paper cites Progressive Neural Networks.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Progressive Neural Networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.175097Z

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Observation 8e11c5a4-d857-4c2b-8787-ebddfe0cc49b · outbound

This paper cites Gradient Projection Memory for Continual Learning.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Gradient Projection Memory for Continual Learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.177849Z

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Observation 28f1826c-f7d1-42a6-a30b-c0610e87d265 · outbound

This paper cites Online Adversarial Purification based on Self-Supervision.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Online Adversarial Purification based on Self-Supervision

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.181112Z

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Observation 4f995678-773d-4fa7-8b7a-c87f497fe335 · outbound

This paper cites One pixel attack for fooling deep neural networks.IEEE Transactions on Evolutionary Computation, 23(5):828–841,.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense One pixel attack for fooling deep neural networks.IEEE Transactions on Evolutionary Computation, 23(5):828–841,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.184040Z

Source-reported events for the cited work

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Observation 8c1f19fd-6f30-4648-9537-6a24ef6de930 · outbound

This paper cites Intriguing properties of neural networks.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Intriguing properties of neural networks

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.188057Z

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Observation 8ea1aa2f-7914-45dd-8377-f5791a66bab2 · outbound

This paper cites Continual learning with hy- pernetworks.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Continual learning with hy- pernetworks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.528050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0071c861-9863-4489-a4e9-d37c7867203f · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense A comprehensive survey of continual learning: Theory, method and application.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.520735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b62133c-282d-4243-888a-e8afde6bba7d · outbound

This paper cites Supermasks in superposition.Advances in Neural Information Processing Systems, 33:15173–15184,.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Supermasks in superposition.Advances in Neural Information Processing Systems, 33:15173–15184,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.513251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f8aaf4fd-63d2-4e1d-ad52-8c3c7ad46672 · outbound

This paper cites Mitigating Adversarial Effects Through Randomization.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Mitigating Adversarial Effects Through Randomization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.198503Z

Source-reported events for the cited work

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Observation ee75e161-adcf-4591-ba40-9e7c9ce8b9fd · outbound

This paper cites Adversarial Purification with the Manifold Hypothesis.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Adversarial Purification with the Manifold Hypothesis

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:22:42.328789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30ee5989-af4e-44ac-a659-53a7a5c9efa8 · outbound

This paper cites Contin- ual learning through synaptic intelligence.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Contin- ual learning through synaptic intelligence

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.204561Z

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Observation a4d4b9dd-134f-4c51-b0de-37a13cb96c27 · outbound

This paper cites Dauphin, and David Lopez-Paz.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Dauphin, and David Lopez-Paz

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.501393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation db13707b-5ee6-4de4-ad5a-27666cb8ccdc · outbound

This paper cites Efficient neural network robustness certifi- cation with general activation functions, 2018.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Efficient neural network robustness certifi- cation with general activation functions, 2018

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.493787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 555be3d0-e79b-4d30-8aa3-7388e2bc2756 · outbound

This paper cites Defense without for- getting: Continual adversarial defense with anisotropic & isotropic pseudo replay.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Defense without for- getting: Continual adversarial defense with anisotropic & isotropic pseudo replay

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.486260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b8350f93-0353-4cc9-8fac-b63b020ce5e0 · outbound

This paper cites Reliable Adversarial Distillation with Unreliable Teachers.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Reliable Adversarial Distillation with Unreliable Teachers

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:42.214601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adc6b153-5297-4113-8dc4-9996a2c7872e · outbound

This paper cites For both Split CIFAR-100 and Split mini- ImageNet, each class appears in only one group, ensuring Table 2.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense For both Split CIFAR-100 and Split mini- ImageNet, each class appears in only one group, ensuring Table 2

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.479002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37624e5c-e634-4b2a-97e1-eda4badf4933 · outbound

This paper cites The AutoAttack configuration is the same as used in the main experiments of the paper.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense The AutoAttack configuration is the same as used in the main experiments of the paper

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:22:42.470693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e8b6ec0-6ee7-48a7-b374-de678cd7374a · outbound

This paper cites Importantly, SHIELD is attack-agnostic - it does not rely on generating adversarial examples during training, in contrast to AIR.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Importantly, SHIELD is attack-agnostic - it does not rely on generating adversarial examples during training, in contrast to AIR

Reference 100

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T05:22:42.463375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e662b25f-788f-454a-bcf4-d4d4e244ca0c · outbound

This paper cites The learning rate scheduler matched that of the Split CIFAR-100 setup.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense The learning rate scheduler matched that of the Split CIFAR-100 setup

Reference 255

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T05:22:42.311667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 189f6cbd-7d2f-4f48-abe4-63aea23ff958 · outbound

This paper cites an unresolved cited work.

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense Unresolved cited work

Reference 2017

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:22:42.585967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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