Paper Citation Record · LEDGER

Towards Class-wise Robustness Analysis

As of 12 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2411.19853.

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

pith.paper-citation-record.v1
2411.19853 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:47:21.141256Z

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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:41:11.385339Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:41:23.571339Z

Reference resolution

37 of 37 outbound references displayed

verified exact1
verified fuzzy24
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca5f9f67-caa0-4973-8859-38422c64b4e1 · outbound

This paper cites CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks.

Towards Class-wise Robustness Analysis CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:20.991304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d7dd3f9-7155-4440-9778-d4fbbac146d3 · outbound

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

Towards Class-wise Robustness Analysis Obfus- cated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.620131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21a8fe5d-427d-4ca3-b667-6440db35025a · outbound

This paper cites Robustness may be at odds with fairness: An empir- ical study on class-wise accuracy.

Towards Class-wise Robustness Analysis Robustness may be at odds with fairness: An empir- ical study on class-wise accuracy

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.609273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f7eb94cf-eb20-4a1d-ad46-9eda099b219f · outbound

This paper cites Adversarial robustness: From self-supervised pre-training to fine-tuning.

Towards Class-wise Robustness Analysis Adversarial robustness: From self-supervised pre-training to fine-tuning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.598210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b582f5f0-8d4b-4c50-8011-dda6da7e4fcf · outbound

This paper cites RobustBench: a standardized adversarial robustness benchmark.

Towards Class-wise Robustness Analysis RobustBench: a standardized adversarial robustness benchmark

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.011738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8f5da52-18a2-4826-bfbe-febff4eb434e · outbound

This paper cites Mind the box: l1-apgd for sparse adversarial attacks on image classifiers.

Towards Class-wise Robustness Analysis Mind the box: l1-apgd for sparse adversarial attacks on image classifiers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.586783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5f36e1c3-b3bc-4e23-957f-3e24c4846794 · outbound

This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Towards Class-wise Robustness Analysis Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.021525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fcddc17-da16-47c6-bcf1-2e200088dc30 · outbound

This paper cites Robust models are less over-confident.

Towards Class-wise Robustness Analysis Robust models are less over-confident

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.025322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e19ec01-b1ba-471a-8a57-97a41797652e · outbound

This paper cites Frequencylowcut pooling-plug and play against catas- trophic overfitting.

Towards Class-wise Robustness Analysis Frequencylowcut pooling-plug and play against catas- trophic overfitting

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.561323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 799d098e-68d6-4735-aeef-d73fed5efa93 · outbound

This paper cites Alias- ing and adversarial robust generalization of cnns.

Towards Class-wise Robustness Analysis Alias- ing and adversarial robust generalization of cnns

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.549071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0fb905dc-fe3d-4fe3-b705-41d4e98710a8 · outbound

This paper cites Alias- ing coincides with CNNs vulnerability towards adversarial attacks.

Towards Class-wise Robustness Analysis Alias- ing coincides with CNNs vulnerability towards adversarial attacks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.536926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fd5d729e-c6f7-4c56-bf19-79fd1103ceb0 · outbound

This paper cites Fix your downsampling asap! be natively more robust via aliasing and spectral artifact free pooling.

Towards Class-wise Robustness Analysis Fix your downsampling asap! be natively more robust via aliasing and spectral artifact free pooling

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.040304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58728992-1698-4a4a-8094-8325ea97a49c · outbound

This paper cites As large as it gets-studying infinitely large convolutions via neural im- plicit frequency filters.

Towards Class-wise Robustness Analysis As large as it gets-studying infinitely large convolutions via neural im- plicit frequency filters

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.525033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 450475c6-c6ec-4358-95d1-f590d3191a14 · outbound

This paper cites Deep residual learning for image recognition.

Towards Class-wise Robustness Analysis Deep residual learning for image recognition

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.047919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c4e94c8-5134-42e7-99c7-7746a02df1c7 · outbound

This paper cites Identity mappings in deep residual networks.

Towards Class-wise Robustness Analysis Identity mappings in deep residual networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.506683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e4b6fa5d-c608-4222-a8aa-5538e5f43eec · outbound

This paper cites Benchmarking neu- ral network robustness to common corruptions and perturba- tions.

Towards Class-wise Robustness Analysis Benchmarking neu- ral network robustness to common corruptions and perturba- tions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.495442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 57b1dcb3-ac0e-4b60-abb5-ba6c3becd04b · outbound

This paper cites Benchmarking neu- ral network robustness to common corruptions and perturba- tions, 2019.

Towards Class-wise Robustness Analysis Benchmarking neu- ral network robustness to common corruptions and perturba- tions, 2019

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.481598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4826937e-27a5-4450-89a0-63a94e049239 · outbound

This paper cites Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space.

Towards Class-wise Robustness Analysis Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:47:21.207043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9e9c4d04-24ea-48c0-887f-fe97473becab · outbound

This paper cites Densely connected convolutional net- works.

Towards Class-wise Robustness Analysis Densely connected convolutional net- works

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.070020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f26b4445-b4a1-4c08-8e9f-4416cff7aab7 · outbound

This paper cites Neural Architecture Design and Robustness: A Dataset.

Towards Class-wise Robustness Analysis Neural Architecture Design and Robustness: A Dataset

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.074550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d79b7872-85bb-4fad-b729-edc36b7f18ff · outbound

This paper cites Learning multiple layers of features from tiny images, 2009.

Towards Class-wise Robustness Analysis Learning multiple layers of features from tiny images, 2009

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.079094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5da9609-5a3a-41fe-855e-7fde7f5c1734 · outbound

This paper cites Adver- sarial machine learning at scale, 2017.

Towards Class-wise Robustness Analysis Adver- sarial machine learning at scale, 2017

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.455975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5fae65a6-4a1d-4ae0-9249-1c871e9d5761 · outbound

This paper cites Interactive image segmentation with first click attention.

Towards Class-wise Robustness Analysis Interactive image segmentation with first click attention

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.444310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 369b697d-73db-4227-a890-67b37dcab98a · outbound

This paper cites Improving native cnn robustness with filter fre- quency regularization.

Towards Class-wise Robustness Analysis Improving native cnn robustness with filter fre- quency regularization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.433503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7c842beb-e9e3-4c44-ba29-b0da1a07b51b · outbound

This paper cites Towards deep learning models resistant to adversarial attacks, 2019.

Towards Class-wise Robustness Analysis Towards deep learning models resistant to adversarial attacks, 2019

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.094743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74224fba-fe56-44f0-8fc9-4cb24ddaac4d · outbound

This paper cites Fair-tat: Improving model fairness using targeted adversarial training.

Towards Class-wise Robustness Analysis Fair-tat: Improving model fairness using targeted adversarial training

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.416013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b7248f42-a64f-49d9-a276-26395c5efc97 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Towards Class-wise Robustness Analysis DINOv2: Learning Robust Visual Features without Supervision

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.102115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8889fb94-5806-4201-8df6-9c8e10caedaf · outbound

This paper cites Boosting adversarial training with hypersphere embedding, 2020.

Towards Class-wise Robustness Analysis Boosting adversarial training with hypersphere embedding, 2020

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.404812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5f7a5e9f-43bb-4a05-b94d-b581f3c248b3 · outbound

This paper cites Duchi, and Percy Liang.

Towards Class-wise Robustness Analysis Duchi, and Percy Liang

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.394069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 120283fd-04b9-469f-81d0-b1d5e2ad9731 · outbound

This paper cites Deep neural networks for object detection.

Towards Class-wise Robustness Analysis Deep neural networks for object detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.383619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f784df0b-8464-4143-aeff-bda7b62db3d1 · outbound

This paper cites Analysis and applications of class-wise robustness in adver- sarial training.

Towards Class-wise Robustness Analysis Analysis and applications of class-wise robustness in adver- sarial training

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.371547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 39ce99fd-52e2-4383-b3a4-26e29f62701c · outbound

This paper cites On the convergence and robustness of adversarial training.

Towards Class-wise Robustness Analysis On the convergence and robustness of adversarial training

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.359312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c2a79672-41be-4bde-8b7c-b8735bebbb6b · outbound

This paper cites Improving adversarial robustness requires revisiting misclassified examples.

Towards Class-wise Robustness Analysis Improving adversarial robustness requires revisiting misclassified examples

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.345827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c4674c7-d375-4534-8f53-c6ef26e72583 · outbound

This paper cites Cfa: Class-wise calibrated fair adversarial training.

Towards Class-wise Robustness Analysis Cfa: Class-wise calibrated fair adversarial training

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.332282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 28155b04-4688-40c5-8dfb-8d65c23c1023 · outbound

This paper cites Ad- versarial examples: Attacks and defenses for deep learning.

Towards Class-wise Robustness Analysis Ad- versarial examples: Attacks and defenses for deep learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.320792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 22480d7b-d197-4cae-b491-e0875e5ef618 · outbound

This paper cites Wide Residual Networks.

Towards Class-wise Robustness Analysis Wide Residual Networks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T05:47:21.137136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa7fc84e-b107-49ca-9e14-acfb3f9edc8c · outbound

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

Towards Class-wise Robustness Analysis Theoretically princi- pled trade-off between robustness and accuracy

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:47:21.307668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation e3feaf45-eb44-4200-bcf3-f11b5f9ebbea · inbound

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification cites this paper.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification Towards Class-wise Robustness Analysis

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:41:23.643382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.