Efficient Adversarial Training via Criticality-Aware Fine-Tuning | Paper Citation Record

Observation 16a1c284-5d64-4d19-a612-8207fe0e03c6 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 1

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This paper cites Available: https://openreview.net/forum?id= YicbFdNTTy 1, 3, 4, 5, 6.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Available: https://openreview.net/forum?id= YicbFdNTTy 1, 3, 4, 5, 6

Reference 2

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This paper cites Contrastive multi-bit collaborative learning for deep cross-modal hash- ing.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Contrastive multi-bit collaborative learning for deep cross-modal hash- ing

Reference 3

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This paper cites Communications of the ACM 65(1), 99–106 (2021) https://doi.org/ 10.1007/978-3-030-58452-8 24.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Communications of the ACM 65(1), 99–106 (2021) https://doi.org/ 10.1007/978-3-030-58452-8 24

Reference 4

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This paper cites Sequential modeling enables scalable learning for large vision models.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Sequential modeling enables scalable learning for large vision models

Reference 5

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Observation da450835-7ebd-437f-9d7e-0da43e71c60f · outbound

This paper cites Walk in the cloud: Learning curves for point clouds shape analysis, pp.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Walk in the cloud: Learning curves for point clouds shape analysis, pp

Reference 6

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This paper cites Walk in the cloud: Learning curves for point clouds shape analysis, pp.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Walk in the cloud: Learning curves for point clouds shape analysis, pp

Reference 7

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Observation 218a1651-61e4-4938-a783-3b643c0bda92 · outbound

This paper cites Diffusion models for adversarial purification.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Diffusion models for adversarial purification

Reference 8

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Observation b8049c18-a7e4-4daa-900b-1b073cfcda98 · outbound

This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Feature squeezing: Detecting adversarial examples in deep neural networks

Reference 9

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Observation f62765db-7260-4419-b2c1-e95f67286d6a · outbound

This paper cites Pixeldefend: Leveraging generative models to understand and defend against adversarial examples.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Pixeldefend: Leveraging generative models to understand and defend against adversarial examples

Reference 10

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Observation 924e2af6-89fd-42e8-9a16-24f19b4dbfb4 · outbound

This paper cites Towards deep learning models resistant to ad- versarial attacks.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Towards deep learning models resistant to ad- versarial attacks

Reference 11

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This paper cites Adversarial weight perturbation helps robust generalization.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Adversarial weight perturbation helps robust generalization

Reference 12

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Observation 7a542dc5-f328-409e-90b9-e64adb470fb1 · outbound

This paper cites Defending against neural network model stealing attacks using deceptive per- turbations.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Defending against neural network model stealing attacks using deceptive per- turbations

Reference 13

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This paper cites Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., and Choi, Y.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., and Choi, Y

Reference 14

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Observation af17f4a1-9dbf-4248-9c3e-be039f1b9341 · outbound

This paper cites Parameter-efficient transfer learning for NLP.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Parameter-efficient transfer learning for NLP

Reference 15

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Observation a507aa2f-99fd-4d2f-a96f-e584631caf46 · outbound

This paper cites Language models are few-shot learners.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Language models are few-shot learners

Reference 16

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Observation 8068d072-a3ba-4425-ae15-e8d1232e5bc9 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning LoRA: Low-rank adaptation of large language models

Reference 17

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Observation 880358fa-637e-4827-9e3e-171b6b361fa9 · outbound

This paper cites FullLoRA: Efficiently Boosting the Robustness of Pretrained Vision Transformers.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning FullLoRA: Efficiently Boosting the Robustness of Pretrained Vision Transformers

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:31:01.943578Z

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Observation 3bae1905-4a66-48d6-b88e-f087758786e9 · outbound

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

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Theoretically principled trade-off between robustness and accuracy

Reference 19

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This paper cites Im- proving adversarial robustness requires revisiting misclassi- fied examples.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Im- proving adversarial robustness requires revisiting misclassi- fied examples

Reference 20

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Observation 35f47cd6-258b-4ecb-81b5-df5b36b8cbd8 · outbound

This paper cites When adver- sarial training meets vision transformers: Recipes from train- ing to architecture.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning When adver- sarial training meets vision transformers: Recipes from train- ing to architecture

Reference 21

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This paper cites Gradient-based learning applied to document recognition.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Gradient-based learning applied to document recognition

Reference 22

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Observation cf068172-1daf-4e76-971e-4051fcf55ba7 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Imagenet classification with deep convolutional neural networks

Reference 23

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This paper cites Segment anything.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Segment anything

Reference 24

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Observation 134a8359-f4b6-47cc-9da8-813850fe15a2 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Masked-attention mask transformer for universal image segmentation

Reference 25

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This paper cites Attention is all you need.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Attention is all you need

Reference 26

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Observation ea142899-5a3a-472f-a829-95acb67cdc37 · outbound

This paper cites The Llama 3 Herd of Models.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning The Llama 3 Herd of Models

Reference 27

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Observation 3dee2b91-9ec8-427a-b6a8-d6595b739ac4 · outbound

This paper cites Elf: An end-to-end local and global multimodal fusion framework for glaucoma grading.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Elf: An end-to-end local and global multimodal fusion framework for glaucoma grading

Reference 28

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This paper cites Facenet: A unified embedding for face recognition and clustering.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Facenet: A unified embedding for face recognition and clustering

Reference 29

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Observation 83074c22-f6ee-4b5d-b6c3-9d87fa765a9a · outbound

This paper cites Adversarial sticker: A stealthy attack method in the physical world.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Adversarial sticker: A stealthy attack method in the physical world

Reference 30

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This paper cites Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$

Reference 31

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Observation 773c97ea-b691-42c0-84d2-2d1a97cf2e0d · outbound

This paper cites Random entangled tokens for adversarially robust vision transformer.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Random entangled tokens for adversarially robust vision transformer

Reference 32

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Observation 2f06f8f1-e7db-48aa-b8f4-1b41f0e8a316 · outbound

This paper cites Towards understanding and improving adversarial robustness of vision transformers.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Towards understanding and improving adversarial robustness of vision transformers

Reference 33

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This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 34

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Observation d362fd2c-4b85-4c51-a9b4-8a530d08db94 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Clip-adapter: Better vision-language models with feature adapters

Reference 35

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This paper cites Towards evaluating the robust- ness of neural networks.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Towards evaluating the robust- ness of neural networks

Reference 36

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Observation 2d18f00e-20e5-4d26-b25d-07f4c73a5e6d · outbound

This paper cites Explaining and harnessing adversarial examples.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Explaining and harnessing adversarial examples

Reference 37

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Observation 3aa0965d-4dac-4043-9d73-ed6a6fcb1d66 · outbound

This paper cites Improv- ing generalization of adversarial training via robust critical fine-tuning.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Improv- ing generalization of adversarial training via robust critical fine-tuning

Reference 38

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This paper cites Importance estimation for neural network pruning.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Importance estimation for neural network pruning

Reference 39

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This paper cites Proxylessnas: Direct neural architecture search on target task and hardware.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Proxylessnas: Direct neural architecture search on target task and hardware

Reference 40

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Observation c57e4c88-33e0-43fd-b1c4-6aeb6c94bc34 · outbound

This paper cites Available: https://openreview.net/forum?id= HylVB3AqYm 4.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Available: https://openreview.net/forum?id= HylVB3AqYm 4

Reference 41

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This paper cites Peft: State-of-the-art parameter-efficient fine- tuning methods.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Peft: State-of-the-art parameter-efficient fine- tuning methods

Reference 42

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This paper cites Multi-LoRA Composition for Image Generation.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Multi-LoRA Composition for Image Generation

Reference 43

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This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Parameter-efficient fine-tuning of large-scale pre-trained language models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T11:32:36.720777Z

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Observation 57a4319c-7c6c-4763-96b8-d8fb60e339b7 · outbound

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

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Learning multiple layers of features from tiny images

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T11:32:36.730955Z

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Observation 4a47bd3c-da1a-443e-9e15-57eef1a5e9cd · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning ImageNet Large Scale Visual Recognition Challenge

Reference 46

Resolution: verified exact; doi, observed 2026-05-10T14:50:29.979273Z

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Observation f60aeb41-d797-4225-804d-27164d99a820 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Swin transformer: Hierarchical vision transformer using shifted windows

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T11:32:36.751677Z

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Observation 1f6a9335-75bf-4c4c-9d89-916f927622ce · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T11:32:36.746448Z

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Observation 2b69b7c4-a854-44ce-ab0d-f19f5a7129d9 · outbound

This paper cites Parameter-efficient Tuning of Large-scale Multimodal Foundation Model.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Parameter-efficient Tuning of Large-scale Multimodal Foundation Model

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:31:01.939920Z

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Observation 0541b58e-e344-413d-a065-6679a0f0f8ab · outbound

This paper cites Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:50:29.975741Z

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Observation 7634f688-0665-431b-83a7-98d9474956ad · outbound

This paper cites Decoupled Weight Decay Regularization.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Decoupled Weight Decay Regularization

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-11T11:31:01.932713Z

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No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7fc28682-5999-4f08-8aa2-63fb4ccdeba9 · outbound

This paper cites Scaling vision transformers to 22 billion parameters.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Scaling vision transformers to 22 billion parameters

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T11:32:36.743922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 07127830-9dee-4ef5-80c5-9474ea75a5db · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning LLaVA-OneVision: Easy Visual Task Transfer

Reference 53

Resolution: metadata mismatch; local_arxiv, observed 2026-05-10T14:50:29.982450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 615835f0-7a78-4e90-bb5c-a926c5a762f0 · outbound

This paper cites an unresolved cited work.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-18T11:32:36.748846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6da63d7f-84cd-4f97-b660-62ed70dd8680 · outbound

This paper cites Experiment Code The source code is available athttps://anonymous.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Experiment Code The source code is available athttps://anonymous

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T11:32:36.733898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 27b49bfd-738f-42f2-94fb-5e69d2cb4546 · outbound

This paper cites Effect of number of adversarial samples We also investigate the effect of varying the number of ad- versarial samples used to calculate parameter criticality.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning Effect of number of adversarial samples We also investigate the effect of varying the number of ad- versarial samples used to calculate parameter criticality

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T11:32:36.736676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 32ce4d0b-f8cf-402b-8e91-ee65c96e1cc4 · outbound

This paper cites It is important to note that enhancing robustness is not the primary objective of this work.

Efficient Adversarial Training via Criticality-Aware Fine-Tuning It is important to note that enhancing robustness is not the primary objective of this work

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T11:32:36.741308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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