Are Fast Methods Stable in Adversarially Robust Transfer Learning? | Paper Citation Record

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This paper cites Under- standing and improving fast adversarial training.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Under- standing and improving fast adversarial training

Reference 1

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Observation 00310702-60d2-4155-8d7b-61742910934f · outbound

This paper cites Square attack: a query-efficient black-box adversarial attack via random search.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Square attack: a query-efficient black-box adversarial attack via random search

Reference 2

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Observation ed0834d0-1dcc-4c9d-b528-6b4d4e0e2827 · outbound

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

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Obfus- cated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 3

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This paper cites Bit- Fit: Simple parameter-efficient fine-tuning for transformer- based masked language-models.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Bit- Fit: Simple parameter-efficient fine-tuning for transformer- based masked language-models

Reference 4

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This paper cites Towards evaluating the robustness of neural networks.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Towards evaluating the robustness of neural networks

Reference 5

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Observation c5915ab5-1b75-44e2-aa74-6df1301ece1d · outbound

This paper cites Unlabeled data improves adver- sarial robustness.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Unlabeled data improves adver- sarial robustness

Reference 6

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Observation 62c4bd7e-620c-48d2-a332-9476b9734145 · outbound

This paper cites Minimally distorted adversarial examples with a fast adaptive boundary attack.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Minimally distorted adversarial examples with a fast adaptive boundary attack

Reference 7

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Observation bc4b6fdb-8295-44a7-b721-9c913d5ff66e · outbound

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

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 8

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Observation 3e66629c-7b27-440f-870a-8894df094521 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Imagenet: A large-scale hierarchical image database

Reference 9

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Observation 37c1afff-d03b-45e1-8151-62573eae2d72 · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Bert: Pre-training of deep bidirectional trans- formers for language understanding

Reference 10

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This paper cites Benchmarking adversar- ial robustness on image classification.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Benchmarking adversar- ial robustness on image classification

Reference 11

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Observation 46d77147-ddf7-4b09-89c7-653eb0219499 · outbound

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

Are Fast Methods Stable in Adversarially Robust Transfer Learning? An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

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Observation 7844bfad-8e2a-4617-adcc-85a9d9071ca1 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Explaining and Harnessing Adversarial Examples

Reference 13

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Observation f22cee50-a357-48f4-81c5-f8d91366e1bf · outbound

This paper cites SAFER: Sharpness Aware layer-selective Finetuning for Enhanced Robustness in vision transformers.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? SAFER: Sharpness Aware layer-selective Finetuning for Enhanced Robustness in vision transformers

Reference 14

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Observation 81c2aba3-f2a1-4186-b9cf-e9a0d7f3d273 · outbound

This paper cites Caltech- 256 object category dataset.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Caltech- 256 object category dataset

Reference 15

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Observation 3aef1f40-7dc3-4bde-b474-e01ff0db0e94 · outbound

This paper cites Countering adversarial images using input transformations.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Countering adversarial images using input transformations

Reference 16

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Observation f99b9dca-014d-40ce-a62c-9d9de36929ec · outbound

This paper cites Deep residual learning for image recognition.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Deep residual learning for image recognition

Reference 17

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Observation 4ff10644-6ae9-4cd0-af9b-e2501db4e9f6 · outbound

This paper cites Us- ing pre-training can improve model robustness and uncer- tainty.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Us- ing pre-training can improve model robustness and uncer- tainty

Reference 18

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This paper cites Parameter-efficient transfer learning for nlp.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Parameter-efficient transfer learning for nlp

Reference 19

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Observation f3e99cff-c5b5-4c9e-9b84-494fc9f74100 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? LoRA: Low-Rank Adaptation of Large Language Models

Reference 20

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Observation 916427ad-7434-4bb4-a2dc-3e659cde5f5b · outbound

This paper cites Initialization matters for adversarial transfer learning.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Initialization matters for adversarial transfer learning

Reference 21

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Observation b40ffa24-0ea8-4130-97a8-1fa19a79c4ca · outbound

This paper cites Prior-guided adversarial initialization for fast adversarial training.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Prior-guided adversarial initialization for fast adversarial training

Reference 22

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Observation ef4076d0-c3d8-4cda-998d-258ddc8f0d5b · outbound

This paper cites Novel dataset for fine-grained image categorization: Stanford dogs.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Novel dataset for fine-grained image categorization: Stanford dogs

Reference 23

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Observation 1059aa00-85ef-476f-a906-c97ad6281fee · outbound

This paper cites Understanding catastrophic overfitting in single-step adversarial training.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Understanding catastrophic overfitting in single-step adversarial training

Reference 24

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Observation 3ef4b045-22ed-4e32-a08b-1a6f0a19d26a · outbound

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

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Learning multiple layers of features from tiny images

Reference 25

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Observation c152ff01-61f2-4992-8e55-c7d9a6144a5d · outbound

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

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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Observation 228c55fb-81f8-41a5-b829-41330e1118b6 · outbound

This paper cites Twins: A fine-tuning framework for improved transferability of ad- versarial robustness and generalization.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Twins: A fine-tuning framework for improved transferability of ad- versarial robustness and generalization

Reference 27

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Observation af8f3723-fa31-44d3-a082-1c1060f84bb1 · outbound

This paper cites Towards deep learn- ing models resistant to adversarial attacks.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Towards deep learn- ing models resistant to adversarial attacks

Reference 28

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Observation 7b351534-95b3-4843-990b-a7674eb7d490 · outbound

This paper cites Automated flower classification over a large number of classes.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Automated flower classification over a large number of classes

Reference 29

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This paper cites Distillation as a defense to adver- sarial perturbations against deep neural networks.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Distillation as a defense to adver- sarial perturbations against deep neural networks

Reference 30

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Observation 7192cc3f-8c56-4bd9-8005-29d5f292048d · outbound

This paper cites Reliably fast adversar- ial training via latent adversarial perturbation.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Reliably fast adversar- ial training via latent adversarial perturbation

Reference 31

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Observation 4b779c3d-7317-4703-a67e-0f055905ece9 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? High-resolution image synthesis with latent diffusion models

Reference 32

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Observation 64e2dd58-e36d-4b7c-9d6d-d041064d9204 · outbound

This paper cites Do adversarially robust im- agenet models transfer better? Advances in Neural Informa- tion Processing Systems, 33:3533–3545, 2020.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Do adversarially robust im- agenet models transfer better? Advances in Neural Informa- tion Processing Systems, 33:3533–3545, 2020

Reference 33

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This paper cites Adversarially robust gener- alization requires more data.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Adversarially robust gener- alization requires more data

Reference 34

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This paper cites Adversarial training for free! Advances in neural information processing systems , 32, 2019.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Adversarial training for free! Advances in neural information processing systems , 32, 2019

Reference 35

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Observation 4e7391c7-b276-4edb-92ea-9c8c480045eb · outbound

This paper cites Adver- sarially robust transfer learning.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Adver- sarially robust transfer learning

Reference 36

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Observation 798d6395-ec77-44b7-b5e1-2407a90c3d84 · outbound

This paper cites On the Adversarial Robustness of Vision Transformers.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? On the Adversarial Robustness of Vision Transformers

Reference 37

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This paper cites Intriguing properties of neural networks.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Intriguing properties of neural networks

Reference 38

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This paper cites Transfer learning.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Transfer learning

Reference 39

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Observation fdfee3b3-d20f-4820-b2d8-1ff85d6e3af0 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? LLaMA: Open and Efficient Foundation Language Models

Reference 40

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Observation 1e88e1ee-4256-49b4-8bc1-d12e8568a987 · outbound

This paper cites Ensemble adversarial training: Attacks and defenses.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Ensemble adversarial training: Attacks and defenses

Reference 41

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Observation 15d2b1d0-b1b9-40d6-8f44-af211dd031f0 · outbound

This paper cites Autonomous vehicle perception: The technology of today and tomorrow.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Autonomous vehicle perception: The technology of today and tomorrow

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:06.050122Z

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Observation 06063711-9cfa-40b2-a760-a44b236ecf99 · outbound

This paper cites Attention is all you need.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Attention is all you need

Reference 43

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Observation e7160f06-4fb2-4454-a3bc-65cfef1b03d5 · outbound

This paper cites Better diffusion models further improve adversarial training.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Better diffusion models further improve adversarial training

Reference 44

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Observation 6b6e41e4-b804-4366-90ad-1d9a3911057e · outbound

This paper cites Preventing catastrophic overfitting in fast adversarial train- ing: A bi-level optimization perspective.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Preventing catastrophic overfitting in fast adversarial train- ing: A bi-level optimization perspective

Reference 45

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This paper cites A survey of transfer learning.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? A survey of transfer learning

Reference 46

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Observation 489054f5-7952-4823-9e51-6cc2de8d72ad · outbound

This paper cites Zico Kolter.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Zico Kolter

Reference 47

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This paper cites Towards efficient adversarial training on vision transformers.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Towards efficient adversarial training on vision transformers

Reference 48

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Observation d37c72dd-c001-4939-af75-7ed7e83d0c08 · outbound

This paper cites Au- tolora: An automated robust fine-tuning framework.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Au- tolora: An automated robust fine-tuning framework

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:05.371402Z

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Observation 9f10d321-6fc1-4b1d-9496-2db9210dbab1 · outbound

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

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Theoretically principled trade-off between robustness and accuracy

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:05.266828Z

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Observation 70b95776-9fc5-43f7-83d8-cdc0d3c5e5d4 · outbound

This paper cites The models are trained om ImageNet- 1K [9] with ε = 4.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? The models are trained om ImageNet- 1K [9] with ε = 4

Reference 53

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Observation 3eecc3a5-e2d2-4757-a8f5-84a146e1e5a6 · outbound

This paper cites Varying epsilon.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Varying epsilon

Reference 54

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This paper cites an unresolved cited work.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Unresolved cited work

Reference 264

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This paper cites an unresolved cited work.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? Unresolved cited work

Reference 2019

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

Coverage vector

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Pith citing papers