Paper Citation Record · LEDGER

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning

As of 12 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 2 inbound Pith citation observations for arXiv:2412.19747.

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

pith.paper-citation-record.v1
2412.19747 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:57:10.249707Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:59:10.638625Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T16:59:12.281657Z

Reference resolution

36 of 36 outbound references displayed

verified exact0
verified fuzzy31
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a36b3f41-761e-4d6e-99ac-445a6469c717 · outbound

This paper cites Intriguing properties of neural networks.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Intriguing properties of neural networks

Reference 1

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Observation c065253d-3be0-4d05-97fa-a98f753ca9b7 · outbound

This paper cites J., Shlens, J., & Szegedy, C.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning J., Shlens, J., & Szegedy, C

Reference 2

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Observation 7e9d2ff9-ec4c-4bd3-a255-35bc21078105 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Towards deep learning models resistant to adversarial attacks

Reference 3

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Observation efd14870-9b70-40f6-9b3c-04ae1674b3f5 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Towards evaluating the robustness of neural networks

Reference 4

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Observation 3cef72b0-b390-494f-a62a-f571c20ffce1 · outbound

This paper cites Ensemble adversarial training: Attacks and defenses.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Ensemble adversarial training: Attacks and defenses

Reference 5

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Observation 07e3187a-2c87-4b13-89a0-602b71670b05 · outbound

This paper cites S., & Doshi-Velez, F.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning S., & Doshi-Velez, F

Reference 6

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Observation 018de63f-7292-4734-9cfe-66e1074cc4f0 · outbound

This paper cites an unresolved cited work.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Unresolved cited work

Reference 7

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Observation 5be36762-d1eb-465e-9109-777c70d1ef03 · outbound

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

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

Reference 8

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Observation c8addd6f-5918-4a3e-81a4-cf61839f1c0b · outbound

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

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Theoretically principled trade-off between robustness and accuracy

Reference 9

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Observation 8c9521eb-03c0-402b-9878-7af305770ad7 · outbound

This paper cites A simple frame- work for contrastive learning of visual representations.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning A simple frame- work for contrastive learning of visual representations

Reference 10

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Observation 06d52375-781d-40b5-bf72-2a642b07ece5 · outbound

This paper cites Performance analysis of cooperative multicell precoding with global CSI and local individual CSI in the large dimen- sional regime,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Performance analysis of cooperative multicell precoding with global CSI and local individual CSI in the large dimen- sional regime,

Reference 11

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Observation 29a4ce62-5e60-4a72-85a9-362c10a684b7 · outbound

This paper cites Low complexity optimization for user centric cellular networks via large dimensional analysis,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Low complexity optimization for user centric cellular networks via large dimensional analysis,

Reference 12

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Observation f98b60af-691e-42a5-8d49-8f8f9135e38e · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Momentum contrast for unsupervised visual representation learning

Reference 13

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Observation d30cfbcc-753e-46a7-8295-39db115af3b4 · outbound

This paper cites Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Reference 14

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Observation 268413b0-8e63-4561-89d7-29e64e2cc634 · outbound

This paper cites Supervised contrastive learning.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Supervised contrastive learning

Reference 15

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Observation d092edd8-0649-457a-b107-7ac0a55e5c71 · outbound

This paper cites Congestion aware dynamic user associa- tion in heterogeneous cellular network: A stochastic decision approach,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Congestion aware dynamic user associa- tion in heterogeneous cellular network: A stochastic decision approach,

Reference 16

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Observation 43f7c645-bb36-40ef-8dd6-40e94c452497 · outbound

This paper cites Explaining the behavior of neuron activations in deep neural networks,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Explaining the behavior of neuron activations in deep neural networks,

Reference 17

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Observation 176dd1d2-c580-405c-a0e6-b64ddb7216c1 · outbound

This paper cites Robust pre-training by adversarial contrastive learning.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Robust pre-training by adversarial contrastive learning

Reference 18

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Observation a2e455c5-91b8-41d0-a615-0e8a0021644c · outbound

This paper cites Large system analysis for densification of cellular networks with massive MIMO,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Large system analysis for densification of cellular networks with massive MIMO,

Reference 19

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Observation abd9bb42-742c-4a2e-ac65-80debe67157a · outbound

This paper cites Layer-wise entropy analysis and visualization of neurons activation,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Layer-wise entropy analysis and visualization of neurons activation,

Reference 20

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Observation 8b18ed83-3a71-4a3c-885d-7dda0061602e · outbound

This paper cites Breathing solitons induced by collision in dipolar Bose-Einstein condensates.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Breathing solitons induced by collision in dipolar Bose-Einstein condensates

Reference 21

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Observation 5daed8f3-f3a0-47bf-b014-d77d771266c0 · outbound

This paper cites Representation learning and nature encoded fusion for heterogeneous sensor networks,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Representation learning and nature encoded fusion for heterogeneous sensor networks,

Reference 22

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Observation a4523e72-77b5-4494-bba5-13216ff2ccc1 · outbound

This paper cites Optimization for user centric massive MIMO cell free networks via large system analysis,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Optimization for user centric massive MIMO cell free networks via large system analysis,

Reference 23

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Observation 7ebeaa57-45d6-4ab7-bbf2-772551fb7b38 · outbound

This paper cites Adversarial training with natural data for improved robustness.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Adversarial training with natural data for improved robustness

Reference 24

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Observation c6be828a-99b1-493b-9a49-414d0cc50139 · outbound

This paper cites Improving adversarial robustness via promoting ensemble diversity.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Improving adversarial robustness via promoting ensemble diversity

Reference 25

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This paper cites Collaborative spectrum sharing based on information pooling for cognitive radio networks with channel heterogeneity,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Collaborative spectrum sharing based on information pooling for cognitive radio networks with channel heterogeneity,

Reference 26

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This paper cites Looking beyond content: Modeling and detection of fake news from a social context perspective,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Looking beyond content: Modeling and detection of fake news from a social context perspective,

Reference 27

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Observation 37fb2a28-d013-444b-ac30-c72cbf9da8e1 · outbound

This paper cites Information theory and representation learning inspired multimodal data fusion,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Information theory and representation learning inspired multimodal data fusion,

Reference 28

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This paper cites Enhanced robustness by symmetry enforcement,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Enhanced robustness by symmetry enforcement,

Reference 29

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Observation a36abe71-bb2c-4bb2-bbf7-e7c82f607fb3 · outbound

This paper cites Partial interference alignment for heterogeneous cellular networks,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Partial interference alignment for heterogeneous cellular networks,

Reference 30

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Observation 27e4f2a7-cf03-4cdb-bac6-1950b12aa227 · outbound

This paper cites Exploration vs exploitation for distributed channel access in cognitive radio networks: A multi-user case study,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Exploration vs exploitation for distributed channel access in cognitive radio networks: A multi-user case study,

Reference 31

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Observation 44b60a0d-daac-4480-b16c-a2843d049ee4 · outbound

This paper cites Deep reinforce- ment learning based computation offloading for mobility-aware edge computing,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Deep reinforce- ment learning based computation offloading for mobility-aware edge computing,

Reference 32

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Observation f9afc3d7-5e4a-43ef-980d-de72be8604af · outbound

This paper cites Improving robustness of deep neural networks via large-difference transformation,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Improving robustness of deep neural networks via large-difference transformation,

Reference 33

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This paper cites DFT-spread based PAPR reduction of OFDM for short reach communication systems,.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning DFT-spread based PAPR reduction of OFDM for short reach communication systems,

Reference 34

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This paper cites MixMatch: A holistic approach to semi-supervised learning.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning MixMatch: A holistic approach to semi-supervised learning

Reference 35

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Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning FixMatch: Simplifying semi- supervised learning with consistency and confidence

Reference 36

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Pith citing papers

Observation 0566d741-fbcb-46b6-a8e0-1952b7f9c5f1 · inbound

Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks cites this paper.

Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning

Reference 36

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Observation 560e8a2e-44dc-4470-a3d9-6adbcdc5c858 · inbound

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI cites this paper.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning

Reference 19

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