Paper Citation Record · LEDGER

Improved Robustness from Biologically Inspired Sparse Contrast Representations

As of 5 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2509.24863.

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

pith.paper-citation-record.v1
2509.24863 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:42:56.681361Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 8f0991bc-1b7c-4290-95dd-994292ae772c · outbound

This paper cites On the unreasonable vulnerability of transformers for image restoration-and an easy fix.

Improved Robustness from Biologically Inspired Sparse Contrast Representations On the unreasonable vulnerability of transformers for image restoration-and an easy fix

Reference 1

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Observation b570c282-4c5a-4e85-b32d-a630ec250726 · outbound

This paper cites Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration

Reference 2

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Observation 9750248a-1283-4403-a2d8-85a0fb85ffc1 · outbound

This paper cites Improving feature stability during upsampling–spectral ar- tifacts and the importance of spatial context.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Improving feature stability during upsampling–spectral ar- tifacts and the importance of spatial context

Reference 3

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Observation c7f0fda6-1c98-40d9-9612-26f8d349d768 · outbound

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

Improved Robustness from Biologically Inspired Sparse Contrast Representations CosPGD: an efficient white-box adversarial attack for pixel- wise prediction tasks

Reference 4

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Observation f7151eb5-b2c3-4b96-b8f1-3d19a8a31fc2 · outbound

This paper cites Roll the dice: Monte carlo downsampling as a low-cost adversarial defence, 2024.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Roll the dice: Monte carlo downsampling as a low-cost adversarial defence, 2024

Reference 5

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Observation 8b53be7b-36f1-4479-ade8-8c728bfc40b1 · outbound

This paper cites Flowbench: Benchmarking optical flow estimation methods for reliability and generalization.Transactions on Machine Learning Research, 2025.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Flowbench: Benchmarking optical flow estimation methods for reliability and generalization.Transactions on Machine Learning Research, 2025

Reference 6

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Observation a687b719-b5fe-4506-9fc5-f7fbf1a41259 · outbound

This paper cites Are synthetic corruptions a reliable proxy for real-world corruptions? In Synthetic Data for Computer Vision Workshop@ CVPR,.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Are synthetic corruptions a reliable proxy for real-world corruptions? In Synthetic Data for Computer Vision Workshop@ CVPR,

Reference 7

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Observation b5373958-0125-4a75-9c23-864384dc132e · outbound

This paper cites On-off center-surround receptive fields for accurate and robust image classification.Inter- national Conference on Machine Learning, pages 478–489,.

Improved Robustness from Biologically Inspired Sparse Contrast Representations On-off center-surround receptive fields for accurate and robust image classification.Inter- national Conference on Machine Learning, pages 478–489,

Reference 8

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Observation a7d9fdaa-0ed4-4237-8c2a-ed2e85aede84 · outbound

This paper cites Explaining, fast and slow: Abstraction and refinement of provable explanations.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Explaining, fast and slow: Abstraction and refinement of provable explanations

Reference 9

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Observation c9c198cd-a4aa-4720-a8a2-c3837cd01042 · outbound

This paper cites King, and Kevin W.

Improved Robustness from Biologically Inspired Sparse Contrast Representations King, and Kevin W

Reference 10

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Observation be881c4e-3a5c-4837-ab32-b4327797c917 · outbound

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Unresolved cited work

Reference 11

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Observation 75ce7bf9-34ad-496a-8393-52d6f96163e5 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 12

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Observation 1b3f11d6-c431-4712-8199-7452123b37c5 · outbound

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

Improved Robustness from Biologically Inspired Sparse Contrast Representations The cityscapes dataset for semantic urban scene understanding

Reference 13

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This paper cites MIT PRESS, [S.l.], 2023.

Improved Robustness from Biologically Inspired Sparse Contrast Representations MIT PRESS, [S.l.], 2023

Reference 14

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Unresolved cited work

Reference 15

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This paper cites A survey of gaussian convolution algo- rithms.Image Processing On Line, 3:286–310, 2013.

Improved Robustness from Biologically Inspired Sparse Contrast Representations A survey of gaussian convolution algo- rithms.Image Processing On Line, 3:286–310, 2013

Reference 16

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This paper cites Optimizing the color-to-grayscale conversion for image classification.Sig- nal, Image and Video Processing, 10(5):853–860, 2016.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Optimizing the color-to-grayscale conversion for image classification.Sig- nal, Image and Video Processing, 10(5):853–860, 2016

Reference 17

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Observation 764dfb73-3c29-4c3a-8813-b022fcc3d019 · outbound

This paper cites Surround modulation: A bio-inspired connectivity structure for convolutional neural networks.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Surround modulation: A bio-inspired connectivity structure for convolutional neural networks

Reference 18

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Observation a5ee513b-6fa3-4338-ba43-0e64b8285218 · outbound

This paper cites Bag of tricks for image classifica- tion with convolutional neural networks.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Bag of tricks for image classifica- tion with convolutional neural networks

Reference 19

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Observation 799e0ad7-c953-4b9d-952a-0edb9b09294d · outbound

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

Improved Robustness from Biologically Inspired Sparse Contrast Representations Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 20

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Observation 778580c4-519f-4889-9eef-eb5c23cdd65b · outbound

This paper cites Towards improving robustness of compressed cnns.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Towards improving robustness of compressed cnns

Reference 21

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Observation 319d43df-ee69-4436-86e3-11aed58e30dc · outbound

This paper cites Impact of Light and Shadow on Robustness of Deep Neural Networks.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Impact of Light and Shadow on Robustness of Deep Neural Networks

Reference 22

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Unresolved cited work

Reference 23

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Observation 4e0ecbd6-6e9f-4514-952b-f7520d0ec062 · outbound

This paper cites Sangjun, Yoonnyun Kim, and Minho Lee.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Sangjun, Yoonnyun Kim, and Minho Lee

Reference 24

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Observation 320c34fa-ba61-46e4-8659-e0ef7886590f · outbound

This paper cites Deep neural networks: A new frame- work for modeling biological vision and brain information processing.Annual review of vision science, 1:417–446,.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Deep neural networks: A new frame- work for modeling biological vision and brain information processing.Annual review of vision science, 1:417–446,

Reference 25

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This paper cites Learning methods for generic object recognition with invariance to pose and lighting.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Learning methods for generic object recognition with invariance to pose and lighting

Reference 26

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Marr and E

Reference 27

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Improved Robustness from Biologically Inspired Sparse Contrast Representations McCulloch and Walter Pitts

Reference 28

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This paper cites Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Reference 29

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Observation ed569e55-8e25-45f0-a7d7-1701b787495d · outbound

This paper cites Mmsegmentation: Open- mmlab semantic segmentation toolbox and benchmark.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Mmsegmentation: Open- mmlab semantic segmentation toolbox and benchmark

Reference 30

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Observation 0b0528be-9dce-4955-9783-f1b2c34183f5 · outbound

This paper cites MIT press, 1999.

Improved Robustness from Biologically Inspired Sparse Contrast Representations MIT press, 1999

Reference 31

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Observation de768bf0-b3c9-4175-9527-e985b2b9f636 · outbound

This paper cites Generalizing biological surround suppression based on center surround similarity via deep neural network models.PLoS compu- tational biology, 19(9):e1011486, 2023.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Generalizing biological surround suppression based on center surround similarity via deep neural network models.PLoS compu- tational biology, 19(9):e1011486, 2023

Reference 32

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This paper cites Classification of plant leaf diseases using deep neural networks in color and grayscale images.Journal of Decision Analytics and Intelligent Computing, 4(1):99– 110, 2024.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Classification of plant leaf diseases using deep neural networks in color and grayscale images.Journal of Decision Analytics and Intelligent Computing, 4(1):99– 110, 2024

Reference 33

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Guided curriculum model adaptation and uncertainty-aware evalua- tion for semantic nighttime image segmentation

Reference 34

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Acdc: The adverse conditions dataset with correspondences for se- mantic driving scene understanding

Reference 35

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This paper cites RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo.

Improved Robustness from Biologically Inspired Sparse Contrast Representations RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo

Reference 36

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Object recogni- tion under lighting variations using pre-trained networks

Reference 37

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Task driven sensor layouts-joint optimiza- tion of pixel layout and network parameters

Reference 38

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Thoreson and Dennis M

Reference 39

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This paper cites Bio-inspired presentation attack detec- tion for face biometrics.Frontiers in computational neuro- science, 13:34, 2019.

Improved Robustness from Biologically Inspired Sparse Contrast Representations Bio-inspired presentation attack detec- tion for face biometrics.Frontiers in computational neuro- science, 13:34, 2019

Reference 40

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Internim- age: Exploring large-scale vision foundation models with deformable convolutions

Reference 41

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Alvarez, and Ping Luo

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Improved Robustness from Biologically Inspired Sparse Contrast Representations Chan- nels

Reference 43

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