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Paper Citation Record · LEDGER

Err on the Side of Texture: Texture Bias on Real Data

As of 11 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2412.10597.

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

pith.paper-citation-record.v1
2412.10597 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:52:26.632555Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

49 of 49 outbound references displayed

  • verified exact6
  • verified fuzzy10
  • unresolved32
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c33b7e7-d82a-460e-92a1-64a3cbb6aaf5 · outbound

This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,.

Err on the Side of Texture: Texture Bias on Real Data ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,

Reference 1

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raw_fallback, observed 2026-08-11T15:52:28.326829Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.337977Z digest=sha256:e51734d2868721d85f386501d95bf1058135e389e14bee4ffb25de2428ae8f0e

Observation 5c91dc5e-0eae-4d51-bdb9-e2379e1dc862 · outbound

This paper cites On the Performance of GoogLeNet and AlexNet Applied to Sketches,.

Err on the Side of Texture: Texture Bias on Real Data On the Performance of GoogLeNet and AlexNet Applied to Sketches,

Reference 2

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source=pdf_text observed=2026-08-11T15:52:26.350551Z digest=sha256:08295e101a2430bb93dcbbff041b8ca6ebe4d6ae342fa5dbb092fa2fc3d0764a

Observation 4329f7f8-6543-4384-9c2d-10d9066ad2a6 · outbound

This paper cites Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet.

Err on the Side of Texture: Texture Bias on Real Data Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

Reference 3

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source=pdf_text observed=2026-08-11T15:52:26.356861Z digest=sha256:ffc5c58550bb57f07f7dbeb16c64a1c8fc147c8c06357b6be34992b45a59b0e0

Observation c59d3b69-846e-4b35-82c4-c3972680ab8d · outbound

This paper cites an unresolved cited work.

Err on the Side of Texture: Texture Bias on Real Data Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-11T15:52:26.363560Z digest=sha256:797f9c24cc981db38b25e90c5452c80ef3b062704122efdf30ea6dca79b615bb

Observation e110e125-20c8-43be-9ecc-9a46756518b0 · outbound

This paper cites Generalisation in humans and deep neural networks.

Err on the Side of Texture: Texture Bias on Real Data Generalisation in humans and deep neural networks

Reference 5

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source=pdf_text observed=2026-08-11T15:52:26.370323Z digest=sha256:e4d6bcdc1d0196e7ee09fa5fae1ac46cc36549dadc88cf3d022c09326c166692

Observation 89a206f6-640a-4b85-ae2a-fb4644bf6ca2 · outbound

This paper cites Natural Adversarial Examples.

Err on the Side of Texture: Texture Bias on Real Data Natural Adversarial Examples

Reference 6

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source=pdf_text observed=2026-08-11T15:52:26.378581Z digest=sha256:3df6d4adb6c5356fc7594e839c634fbf3befe5bc48d74284aca9f5d9d373d075

Observation 5c0abc14-7441-4b7a-8683-4b0abb2711ea · outbound

This paper cites On Synthetic Texture Datasets: Challenges, Creation, and Curation.

Err on the Side of Texture: Texture Bias on Real Data On Synthetic Texture Datasets: Challenges, Creation, and Curation

Reference 7

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local_arxiv, observed 2026-08-11T15:52:27.068505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.385689Z digest=sha256:cd9f1728a69fb8515fb6dcc30a8eb139ecf9f4a9a331276d5bd4c36642b1ff06

Observation af57db43-fdf5-49fb-8191-8055f3d40a4f · outbound

This paper cites Explorations in Texture Learning.

Err on the Side of Texture: Texture Bias on Real Data Explorations in Texture Learning

Reference 8

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local_arxiv, observed 2026-08-11T15:52:27.030994Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.392512Z digest=sha256:52652327f448cd2532d3e47e4e3077714c9bf18b8d81ff868e08ef773b15835b

Observation a40632bb-516b-4a2b-9933-d3bdd13ab210 · outbound

This paper cites Describing Textures in the Wild,.

Err on the Side of Texture: Texture Bias on Real Data Describing Textures in the Wild,

Reference 9

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.398159Z digest=sha256:33e67e8a0c029997aee108be83f74f97c3cf52f66066c48387a0d9bc7b572175

Observation d15cb385-ba2f-4188-872f-2b8f00c606e4 · outbound

This paper cites Shortcut Learning in Deep Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data Shortcut Learning in Deep Neural Networks

Reference 10

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source=pdf_text observed=2026-08-11T15:52:26.405398Z digest=sha256:2a58ce106ebe7f5b59e1d91ead58051b5935a6117d78099b9a2104f05f5190d8

Observation 75a241e4-7543-4a9d-a02b-e5f8a3ef7987 · outbound

This paper cites Intriguing properties of neural networks.

Err on the Side of Texture: Texture Bias on Real Data Intriguing properties of neural networks

Reference 11

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source=pdf_text observed=2026-08-11T15:52:26.422659Z digest=sha256:2118a3acffbbb679a6d3eb8cdc2ea4b0bf78a758084ac7e2ffb8e82fb799f934

Observation b77dbddb-40e6-4a46-a0aa-f9a2d41a481c · outbound

This paper cites Evasion Attacks against Machine Learning at Test Time,.

Err on the Side of Texture: Texture Bias on Real Data Evasion Attacks against Machine Learning at Test Time,

Reference 12

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doi, observed 2026-08-11T15:52:26.980564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.428605Z digest=sha256:5072bee585fabbed18c940b53b5ede18aa194ebadd8f4ca28eddfae171b6c21f

Observation 6a0b02af-5b34-46b3-9b33-d9bdbb39857f · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Err on the Side of Texture: Texture Bias on Real Data Explaining and Harnessing Adversarial Examples

Reference 13

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source=pdf_text observed=2026-08-11T15:52:26.440555Z digest=sha256:9946a9347b4d8dd00e3ad11f4eb3bad456e08ec0903c81913400f612790043fc

Observation 7e5882e6-3728-4095-a42d-5670b0e1ce33 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Err on the Side of Texture: Texture Bias on Real Data Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 14

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source=pdf_text observed=2026-08-11T15:52:26.444982Z digest=sha256:d1db41290593b01644de73800bb5ede7ed2ecc3a74ab574f1d599d01671f4bbc

Observation ec6fb53f-f122-4aa4-9edf-d1e5bd5a6a27 · outbound

This paper cites DeepFool: a simple and accurate method to fool deep neural networks.

Err on the Side of Texture: Texture Bias on Real Data DeepFool: a simple and accurate method to fool deep neural networks

Reference 15

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source=pdf_text observed=2026-08-11T15:52:26.451693Z digest=sha256:3ee6524ca022af281ffd39b4c80944baa42a1be976a6782efd5c8b3f9a25281c

Observation be6d8316-915d-46b7-abcb-2b544e54077a · outbound

This paper cites The Space of Adversarial Strategies.

Err on the Side of Texture: Texture Bias on Real Data The Space of Adversarial Strategies

Reference 16

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.461891Z digest=sha256:7d61a5c853ae5baa2f5f871e1b09aabef1bd566d430d88b9f6e58840b2208309

Observation 91f74a3d-40f8-47dd-bcf9-5a2b45878f17 · outbound

This paper cites Towards Evaluating the Robustness of Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data Towards Evaluating the Robustness of Neural Networks

Reference 17

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source=pdf_text observed=2026-08-11T15:52:26.468603Z digest=sha256:c0b5770b0a2b6072eb96b1999a681ed2fa78a19efbf737ad29a7cb3412cae4bc

Observation 04d96936-4dd1-4f02-aaae-cb3846c96023 · outbound

This paper cites The Limitations of Deep Learning in Adversarial Settings.

Err on the Side of Texture: Texture Bias on Real Data The Limitations of Deep Learning in Adversarial Settings

Reference 18

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source=pdf_text observed=2026-08-11T15:52:26.474789Z digest=sha256:5b62e1d9419a465fd91d71080d29ca42643b89bd31688a785524ad83b6bd5c1a

Observation 94f28d4f-5e3b-40c1-8234-4ac7b5547a08 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Err on the Side of Texture: Texture Bias on Real Data ImageNet Large Scale Visual Recognition Challenge

Reference 19

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source=pdf_text observed=2026-08-11T15:52:26.480448Z digest=sha256:46f13773b0ffafb7cbf42e5390f8b5c717ee394384291cb2de735ab7199a162a

Observation fefdb380-2ec0-4092-aea6-3e353067d4a2 · outbound

This paper cites Torchvision the machine- vision package of torch,.

Err on the Side of Texture: Texture Bias on Real Data Torchvision the machine- vision package of torch,

Reference 20

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source=pdf_text observed=2026-08-11T15:52:26.485651Z digest=sha256:65a278b0f093129b181210379503eb69e466712606091fe89875d5f9ad80b97d

Observation 9b955127-eb42-441c-8cd4-5daa6ade0ac9 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Err on the Side of Texture: Texture Bias on Real Data Deep Residual Learning for Image Recognition

Reference 21

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source=pdf_text observed=2026-08-11T15:52:26.490658Z digest=sha256:9b02a787e6ad2b4db6e29717006d9fc6036577f1bedefd035bad3223109110bd

Observation ed0c1030-afde-479c-a0f6-6a92c6df4463 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 22

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source=pdf_text observed=2026-08-11T15:52:26.495913Z digest=sha256:2f6c53e6c99052098e367e1921e7db22c6269d1f4925c30ab4879e2eac884669

Observation 594306ed-a5be-46fc-8dcb-7b8fa966c688 · outbound

This paper cites Densely Connected Convolutional Networks.

Err on the Side of Texture: Texture Bias on Real Data Densely Connected Convolutional Networks

Reference 23

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source=pdf_text observed=2026-08-11T15:52:26.501289Z digest=sha256:897008d0fe26b3ced6cf8629435121bb1d2c209e99d7cc2d4271ae8deed8e9fd

Observation 31fe393d-23e2-4d7a-9bfd-689051c54aa5 · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision.

Err on the Side of Texture: Texture Bias on Real Data Rethinking the Inception Architecture for Computer Vision

Reference 24

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source=pdf_text observed=2026-08-11T15:52:26.506203Z digest=sha256:7448b1c3fdb3cc696699d7709989c28bda80b84a0743a93b7647585de55744f3

Observation 1185e7bf-2189-4860-8b68-38bc8acd6580 · outbound

This paper cites Evasion Attacks against Machine Learning at Test Time.

Err on the Side of Texture: Texture Bias on Real Data Evasion Attacks against Machine Learning at Test Time

Reference 25

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source=pdf_text observed=2026-08-11T15:52:26.434511Z digest=sha256:1222a7e4c1d5afc2c06ec045e8f1a9a1f89e3d4f881aef7dbd5238dbca38b0ee

Observation ddcc4713-9ca9-4e3b-ad48-0b34037a9002 · outbound

This paper cites The Origins and Prevalence of Texture Bias in Convolutional Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data The Origins and Prevalence of Texture Bias in Convolutional Neural Networks

Reference 26

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source=pdf_text observed=2026-08-11T15:52:26.518545Z digest=sha256:eac1cb6d2ac19dd08ce756819058fbed92b5f412d7ab80898f4a1f413092ae13

Observation fe9fa969-caf9-499f-993b-3977a3980de6 · outbound

This paper cites A ConvNet for the 2020s.

Err on the Side of Texture: Texture Bias on Real Data A ConvNet for the 2020s

Reference 27

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source=pdf_text observed=2026-08-11T15:52:26.512752Z digest=sha256:0c809bd28552a8d1dbb8f49728c6f9921a1b526434824dffad140a527902c345

Observation 18fd791d-34c7-46b8-b821-54d328646a67 · outbound

This paper cites Texture Synthesis Using Convolutional Neural Networks.

Err on the Side of Texture: Texture Bias on Real Data Texture Synthesis Using Convolutional Neural Networks

Reference 28

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source=pdf_text observed=2026-08-11T15:52:26.528372Z digest=sha256:9c9d0bbbaafc5b2d43754a0ef18aa060bda4ca32f46d0c9a8294f833d86bd8b2

Observation 6361632f-3217-4de5-b103-5ecf4da375be · outbound

This paper cites Network Dissection: Quantifying Interpretability of Deep Visual Representations,.

Err on the Side of Texture: Texture Bias on Real Data Network Dissection: Quantifying Interpretability of Deep Visual Representations,

Reference 29

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source=pdf_text observed=2026-08-11T15:52:26.523437Z digest=sha256:c9b623269121e0193803c36e4aa2c0aa399b17f45730c7d8d98f69fe243499d2

Observation 0eb8b3b3-1459-4d26-bc10-81e885d4dcfb · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Err on the Side of Texture: Texture Bias on Real Data Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 30

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source=pdf_text observed=2026-08-11T15:52:26.537998Z digest=sha256:bdaf76dca5025f3d459ae7139246eecda1ac02950e5b827acee16e59cd2a69e9

Observation bff59440-2147-4e75-bd1b-259e787c3781 · outbound

This paper cites Image Style Transfer Using Convolutional Neural Networks,.

Err on the Side of Texture: Texture Bias on Real Data Image Style Transfer Using Convolutional Neural Networks,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.533581Z digest=sha256:c101bc55c9f118dfe125d470d4f2355f8836b1bcbaf1d5c125ac1740fe2ac2b9

Observation 2092ba5e-b3fb-4de2-80cf-4b73c318b8d8 · outbound

This paper cites Shape-Texture Debiased Neural Network Training,.

Err on the Side of Texture: Texture Bias on Real Data Shape-Texture Debiased Neural Network Training,

Reference 32

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raw_fallback, observed 2026-08-11T15:52:28.274082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.551498Z digest=sha256:ee22f7e0b2a6cf039e0882da7b891fa8d3f23bfc3df3d52cad32ac6db6a299e2

Observation 8d6b10f3-5998-487b-917d-b21088b19e22 · outbound

This paper cites Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition,.

Err on the Side of Texture: Texture Bias on Real Data Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition,

Reference 33

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source=pdf_text observed=2026-08-11T15:52:26.542697Z digest=sha256:dcd9f5937bd6c6f5d81be9740b707709c69d2ec3ac7ddb337c1976c99b2b172b

Observation 9a5dc4ef-778e-4e32-9b48-cfcf18488b35 · outbound

This paper cites Adversarial Machine Learning at Scale,.

Err on the Side of Texture: Texture Bias on Real Data Adversarial Machine Learning at Scale,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.561532Z digest=sha256:8a19f5a9d39955cf93cac3f28905fd0441be8dda1775651260d891ad2f7aecee

Observation 4754d572-cb5c-45ba-96c5-a3cbd2b471b3 · outbound

This paper cites Learning with a Strong Adversary.

Err on the Side of Texture: Texture Bias on Real Data Learning with a Strong Adversary

Reference 35

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source=pdf_text observed=2026-08-11T15:52:26.556374Z digest=sha256:b486cdc2ab5272846d594ee485524234f9cb8904263b681316a8b17a9f59201d

Observation ccdef954-7e9c-435b-b175-bd770bcef987 · outbound

This paper cites A Neural Algorithm of Artistic Style.

Err on the Side of Texture: Texture Bias on Real Data A Neural Algorithm of Artistic Style

Reference 36

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source=pdf_text observed=2026-08-11T15:52:26.578368Z digest=sha256:0fdf4f61b553243e97d78cb0c387a5ff1ab7f4902411b3e14f1007cd903eca48

Observation 3214496b-2ba9-4195-8168-8e225b2ef522 · outbound

This paper cites Deep Learning based Feature Ex- traction for Texture Classification,.

Err on the Side of Texture: Texture Bias on Real Data Deep Learning based Feature Ex- traction for Texture Classification,

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.583830Z digest=sha256:dee053c073e0c8fc483679472f2a413a3b094d29b7e4183abef272679088193b

Observation 91019e4c-bc43-4e1a-8b6f-52ffebc7f0a5 · outbound

This paper cites Interpreting Adversarially Trained Convolutional Neural Networks,.

Err on the Side of Texture: Texture Bias on Real Data Interpreting Adversarially Trained Convolutional Neural Networks,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.223732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.573517Z digest=sha256:990769669393eba649f1401ebcc32fbd69abd5a4033d8719ce3748d6e36aea0f

Observation c937c8a8-ca86-408a-a666-f146cba31b83 · outbound

This paper cites Explore the Transfor- mation Space for Adversarial Images,.

Err on the Side of Texture: Texture Bias on Real Data Explore the Transfor- mation Space for Adversarial Images,

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T15:52:26.593136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:52:26.593136Z digest=sha256:d8dcaadbaada393050134cc6948bbb4cfb7244bb98ba809a34236e1acdbee5e3

Observation a7f679b4-eb63-4416-8a3e-5171f64c9092 · outbound

This paper cites Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such Attacks.

Err on the Side of Texture: Texture Bias on Real Data Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such Attacks

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:52:26.705744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.598772Z digest=sha256:1a2bac40870c464a5730a6344a40f12ee2e322bf20468a86e26bf0e83547e577

Observation 8e79487c-0582-4ecf-913a-a72631b9ea8d · outbound

This paper cites Color encoding in biologically-inspired convolutional neural networks,.

Err on the Side of Texture: Texture Bias on Real Data Color encoding in biologically-inspired convolutional neural networks,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.169889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.588636Z digest=sha256:4959953624e9b332eccd0a06f1f23b620e4bbcee6dd7cf9d96508bfe8af1ee47

Observation 84eb2037-085b-4401-be70-3a16eb5689f9 · outbound

This paper cites Universal adversarial perturbations.

Err on the Side of Texture: Texture Bias on Real Data Universal adversarial perturbations

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T15:52:26.604664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:52:26.604664Z digest=sha256:75cd7695cf2c9f235673ba00942276b1363baaa03f5115bf092f5e538bb64b83

Observation 6eb77072-e224-4f3e-918d-73528c645dd9 · outbound

This paper cites These files contain the necessary instructions, images, and the script you will run for this study.

Err on the Side of Texture: Texture Bias on Real Data These files contain the necessary instructions, images, and the script you will run for this study

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.144770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.609588Z digest=sha256:d92835d64b6d76fa6b9f6772b1956066adca712d857b0e7e2957c7b400bff9c9

Observation 9f4389e0-ca44-4906-b48e-fad30f24bde9 · outbound

This paper cites python3 eval_packages.py package_num The script will display 100 images, one at a time in a pop-up window along with four words in the terminal.

Err on the Side of Texture: Texture Bias on Real Data python3 eval_packages.py package_num The script will display 100 images, one at a time in a pop-up window along with four words in the terminal

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.126106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.614679Z digest=sha256:b6faf0a6b9dc876d03fe46ee2768c4e02f7c7281994abbf5f33fdae6e01a83c7

Observation 7aa95d8e-6622-4fdd-82f9-398145d45cea · outbound

This paper cites Your task is to input the number corre- sponding to the texture that you believe is most prominent in the image.

Err on the Side of Texture: Texture Bias on Real Data Your task is to input the number corre- sponding to the texture that you believe is most prominent in the image

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.107129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.620617Z digest=sha256:d77a8b7a9384ca06d5600600076fc533888c06b59b34e99afddedb08fdcb8ddd

Observation d5ca3f70-e403-4917-be0f-1180dbf7ba07 · outbound

This paper cites an unresolved cited work.

Err on the Side of Texture: Texture Bias on Real Data Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-11T15:52:28.083175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.626592Z digest=sha256:df1a01e959f785c40b1fc83de1549fe63723115d031671d6d8566b3f69d02b11

Observation 219a4dcb-9dfd-4b9a-9ffc-95b06d8b817a · outbound

This paper cites an unresolved cited work.

Err on the Side of Texture: Texture Bias on Real Data Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-11T15:52:28.057522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.632555Z digest=sha256:efb6a9d91167b16e90752e51c87009b22746a5813c3b8e271fe4ee4bfeac1418

Observation b4f2d358-8757-419c-a22f-0b5ca8066960 · outbound

This paper cites Adversarial Machine Learning at Scale.

Err on the Side of Texture: Texture Bias on Real Data Adversarial Machine Learning at Scale

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-11T15:52:26.567946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:52:26.567946Z digest=sha256:98a11a1fa164c752228c0aa1b26fb29a1d6d2047096979eb354f891ade3f7b70

Observation 05e4e3b2-0280-45a3-b61c-e827ebbdac92 · outbound

This paper cites Available: http://arxiv.org/abs/1811.

Err on the Side of Texture: Texture Bias on Real Data Available: http://arxiv.org/abs/1811

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:52:28.303749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T15:52:26.345199Z digest=sha256:5c0bee08a4d8ce5dcfb5042f8fe6e7b7122ec6827b880a3b9dd75b7a906f2b75

Pith citing papers

No inbound Pith citation observations are available.