Paper Citation Record · LEDGER

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation

As of 13 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.11608.

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pith.paper-citation-record.v1
2412.11608 v1

Coverage vector

measured 44 of 44 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation a400edac-6e71-458d-9cc5-0b1b43195316 · outbound

This paper cites Inspect, understand, overcome: A survey of practical methods for ai safety,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Inspect, understand, overcome: A survey of practical methods for ai safety,

Reference 1

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This paper cites Intriguing properties of neural networks,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Intriguing properties of neural networks,

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Observation aa9c4e2a-950e-4e12-ad6d-cd3d9195c69c · outbound

This paper cites Explaining and Harnessing Adversarial Examples,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Explaining and Harnessing Adversarial Examples,

Reference 3

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Observation 1db0d8d5-4d56-4d76-b309-3ee3dfb839f6 · outbound

This paper cites Robustness to adversarial examples through an ensemble of specialists,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Robustness to adversarial examples through an ensemble of specialists,

Reference 4

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This paper cites Improving Adversarial Robustness of Ensembles with Diversity Training.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Improving Adversarial Robustness of Ensembles with Diversity Training

Reference 5

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Observation ec6d5803-ac6e-4705-a93f-ae915026f431 · outbound

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

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Improving adversarial ro- bustness via promoting ensemble diversity,

Reference 6

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Observation 02d07cbe-307f-4f2b-95eb-0f12adcf925d · outbound

This paper cites Adaptive mixtures of local experts,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Adaptive mixtures of local experts,

Reference 7

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This paper cites Outrageously large neural networks: The sparsely- gated mixture-of-experts layer,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Outrageously large neural networks: The sparsely- gated mixture-of-experts layer,

Reference 8

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Observation f2bc47e1-749b-4e95-b13d-05b170ca62f5 · outbound

This paper cites Sparsely- gated mixture-of-expert layers for cnn interpretability,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Sparsely- gated mixture-of-expert layers for cnn interpretability,

Reference 9

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Observation b071a2c8-417e-4e7b-a1bb-637f7e9703ac · outbound

This paper cites Deep mixture of experts via shallow embedding,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Deep mixture of experts via shallow embedding,

Reference 10

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Observation 8fe470c1-d70f-48d0-bc46-11cc97b82a24 · outbound

This paper cites On the adversarial robustness of mixture of experts,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation On the adversarial robustness of mixture of experts,

Reference 11

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This paper cites Robust mixture-of-expert training for convolu- tional neural networks,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Robust mixture-of-expert training for convolu- tional neural networks,

Reference 12

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This paper cites Using mixture of expert models to gain insights into semantic segmentation,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Using mixture of expert models to gain insights into semantic segmentation,

Reference 13

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Observation 22b6f030-b72c-41dd-ae0d-937b2d534278 · outbound

This paper cites Evaluating mixture-of- experts architectures for network aggregation,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Evaluating mixture-of- experts architectures for network aggregation,

Reference 14

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This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 15

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This paper cites Deepspeed-moe: Advancing mixture-of- experts inference and training to power next-generation AI scale,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Deepspeed-moe: Advancing mixture-of- experts inference and training to power next-generation AI scale,

Reference 16

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This paper cites Network of experts for large-scale image categorization,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Network of experts for large-scale image categorization,

Reference 17

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Observation ae73bc2d-fcc9-4f7d-a319-3c380abdde2d · outbound

This paper cites Adversarial examples for semantic image segmentation,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Adversarial examples for semantic image segmentation,

Reference 18

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Adversarial examples in the physical world,

Reference 19

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Observation e2ef69d0-71b1-4acb-92cb-f27963aea0e9 · outbound

This paper cites On the robustness of semantic segmentation models to adversarial attacks,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation On the robustness of semantic segmentation models to adversarial attacks,

Reference 20

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This paper cites The pascal visual object classes (VOC) challenge,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation The pascal visual object classes (VOC) challenge,

Reference 21

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This paper cites The cityscapes dataset for semantic urban scene understanding,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation The cityscapes dataset for semantic urban scene understanding,

Reference 22

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This paper cites Universal adversar- ial perturbations against semantic image segmentation,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Universal adversar- ial perturbations against semantic image segmentation,

Reference 23

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This paper cites Adversarial Examples on Segmentation Models Can be Easy to Transfer.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Adversarial Examples on Segmentation Models Can be Easy to Transfer

Reference 24

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Pyramid scene parsing network,

Reference 25

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Observation 68cb89d1-0e6c-481b-9f05-bc0b981decdf · outbound

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Learning multiple layers of features from tiny images,

Reference 27

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Deepfool: A simple and accurate method to fool deep neural networks,

Reference 28

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Ensemble methods in machine learning,

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Improving robustness and calibration in ensembles with diversity regularization,

Reference 31

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Measuring ensemble diversity and its effects on model robustness,

Reference 32

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Synergy-of-experts: Collaborate to improve adversarial robustness,

Reference 34

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense

Reference 35

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Towards deep learning models resistant to adversarial attacks,

Reference 36

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Adversarial risk and the dangers of evaluating against weak attacks,

Reference 37

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Towards evaluating the robustness of neural networks,

Reference 38

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Adam: A method for stochastic optimization,

Reference 39

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This paper cites Univer- sal Adversarial Perturbations,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Univer- sal Adversarial Perturbations,

Reference 40

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This paper cites Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes,

Reference 41

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This paper cites Encoder- decoder with atrous separable convolution for semantic image segmenta- tion,.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Encoder- decoder with atrous separable convolution for semantic image segmenta- tion,

Reference 42

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Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation Deep residual learning for image recognition,

Reference 43

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Observation c2c577f6-13ab-4e30-9644-b56faac56f71 · outbound

This paper cites A2D2: Audi Autonomous Driving Dataset.

Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation A2D2: Audi Autonomous Driving Dataset

Reference 44

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