Paper Citation Record · LEDGER

Generating visual explanations from deep networks using implicit neural representations

As of 12 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2501.11784.

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2501.11784 v1

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Outbound references

Observation c6d5a9ad-64d6-4b94-8924-b39027dc4ddb · outbound

This paper cites Going off-grid: continuous implicit neural representations for 3d vascular modeling.

Generating visual explanations from deep networks using implicit neural representations Going off-grid: continuous implicit neural representations for 3d vascular modeling

Reference 1

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This paper cites Explainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelligence.

Generating visual explanations from deep networks using implicit neural representations Explainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelligence

Reference 2

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Observation 9c5c079a-4867-410c-9aeb-af13ae218e40 · outbound

This paper cites On pixel-wise explanations for non-linear classi- fier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015.

Generating visual explanations from deep networks using implicit neural representations On pixel-wise explanations for non-linear classi- fier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015

Reference 3

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Observation cce96b2e-cf42-4e7e-91f3-20053e141be1 · outbound

This paper cites Seeing implicit neural representations as fourier series.

Generating visual explanations from deep networks using implicit neural representations Seeing implicit neural representations as fourier series

Reference 4

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Observation 43cf7e87-a5d5-4c76-81ef-e4766c0997eb · outbound

This paper cites Implicit neural representations for joint decom- position and registration of gene expression images in the marmoset brain.

Generating visual explanations from deep networks using implicit neural representations Implicit neural representations for joint decom- position and registration of gene expression images in the marmoset brain

Reference 5

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Observation 181f1147-52db-49ef-8de8-8785d83a18fa · outbound

This paper cites Grad-cam++: General- ized gradient-based visual explanations for deep convolu- tional networks.

Generating visual explanations from deep networks using implicit neural representations Grad-cam++: General- ized gradient-based visual explanations for deep convolu- tional networks

Reference 6

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Observation 7acbc904-b28e-4bde-916e-45868a6ccb02 · outbound

This paper cites Real time image saliency for black box classifiers.

Generating visual explanations from deep networks using implicit neural representations Real time image saliency for black box classifiers

Reference 7

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Observation be91f6c9-2b27-43ef-9d0d-715c5e81c34d · outbound

This paper cites The pascal visual object classes challenge: A retrospective.

Generating visual explanations from deep networks using implicit neural representations The pascal visual object classes challenge: A retrospective

Reference 8

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This paper cites Unified implicit neural styliza- tion.

Generating visual explanations from deep networks using implicit neural representations Unified implicit neural styliza- tion

Reference 9

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Observation 7204e114-e854-4a11-9d1f-d7770adef85c · outbound

This paper cites Un- derstanding deep networks via extremal perturbations and smooth masks.

Generating visual explanations from deep networks using implicit neural representations Un- derstanding deep networks via extremal perturbations and smooth masks

Reference 10

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Observation 6681cc6b-5f83-4e64-bf01-a15d4e0a9172 · outbound

This paper cites Interpretable explana- tions of black boxes by meaningful perturbation.

Generating visual explanations from deep networks using implicit neural representations Interpretable explana- tions of black boxes by meaningful perturbation

Reference 11

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Observation 7a92371a-b4e8-482a-91e5-ba857e17e53a · outbound

This paper cites Large-scale unsu- pervised semantic segmentation.

Generating visual explanations from deep networks using implicit neural representations Large-scale unsu- pervised semantic segmentation

Reference 12

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Observation e7565e2f-2937-4c26-93c6-427457f914a8 · outbound

This paper cites Neu- ral tangent kernel: Convergence and generalization in neural networks.

Generating visual explanations from deep networks using implicit neural representations Neu- ral tangent kernel: Convergence and generalization in neural networks

Reference 13

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This paper cites Cameras: Enhanced reso- lution and sanity preserving class activation mapping for im- age saliency.

Generating visual explanations from deep networks using implicit neural representations Cameras: Enhanced reso- lution and sanity preserving class activation mapping for im- age saliency

Reference 14

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This paper cites Adam: A Method for Stochastic Optimization.

Generating visual explanations from deep networks using implicit neural representations Adam: A Method for Stochastic Optimization

Reference 15

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Observation abc5b9e8-c3a8-4df1-a727-0e985cf2e00c · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.

Generating visual explanations from deep networks using implicit neural representations Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 16

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This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

Generating visual explanations from deep networks using implicit neural representations V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 17

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This paper cites Implicit neural representation in medical imaging: A comparative survey.

Generating visual explanations from deep networks using implicit neural representations Implicit neural representation in medical imaging: A comparative survey

Reference 18

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This paper cites Neural image representations for multi-image fusion and layer separation.

Generating visual explanations from deep networks using implicit neural representations Neural image representations for multi-image fusion and layer separation

Reference 19

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This paper cites Deepsdf: Learning con- tinuous signed distance functions for shape representation.

Generating visual explanations from deep networks using implicit neural representations Deepsdf: Learning con- tinuous signed distance functions for shape representation

Reference 20

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Generating visual explanations from deep networks using implicit neural representations Pytorch: An imperative style, high-performance deep learning library

Reference 21

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Observation 0fe3d37c-633c-4a3b-8c9e-32fa53311409 · outbound

This paper cites Rise: Random- ized input sampling for explanation of black-box models.

Generating visual explanations from deep networks using implicit neural representations Rise: Random- ized input sampling for explanation of black-box models

Reference 22

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This paper cites H2o: Heatmap by hierarchical occlusion.

Generating visual explanations from deep networks using implicit neural representations H2o: Heatmap by hierarchical occlusion

Reference 23

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This paper cites Ablation-cam: Visual explanations for deep convolutional network via gradient- free localization.

Generating visual explanations from deep networks using implicit neural representations Ablation-cam: Visual explanations for deep convolutional network via gradient- free localization

Reference 24

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This paper cites A systematic review of ex- plainable artificial intelligence models and applications: Re- cent developments and future trends.

Generating visual explanations from deep networks using implicit neural representations A systematic review of ex- plainable artificial intelligence models and applications: Re- cent developments and future trends

Reference 25

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This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Generating visual explanations from deep networks using implicit neural representations Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 26

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This paper cites One explanation is not enough: structured attention graphs for image classification.Advances in Neural Information Processing Systems, 34:11352–11363, 2021.

Generating visual explanations from deep networks using implicit neural representations One explanation is not enough: structured attention graphs for image classification.Advances in Neural Information Processing Systems, 34:11352–11363, 2021

Reference 27

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This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Generating visual explanations from deep networks using implicit neural representations Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 28

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This paper cites Poly- nomial implicit neural representations for large diverse datasets.

Generating visual explanations from deep networks using implicit neural representations Poly- nomial implicit neural representations for large diverse datasets

Reference 29

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Generating visual explanations from deep networks using implicit neural representations Metasdf: Meta-learning signed distance functions

Reference 30

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Generating visual explanations from deep networks using implicit neural representations Implicit neural representa- tions with periodic activation functions

Reference 31

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Generating visual explanations from deep networks using implicit neural representations SmoothGrad: removing noise by adding noise

Reference 32

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This paper cites Striving for Simplicity: The All Convolutional Net.

Generating visual explanations from deep networks using implicit neural representations Striving for Simplicity: The All Convolutional Net

Reference 33

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Generating visual explanations from deep networks using implicit neural representations Nisf: Neural implicit segmen- tation functions

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This paper cites Ground truth based comparison of saliency maps algorithms.

Generating visual explanations from deep networks using implicit neural representations Ground truth based comparison of saliency maps algorithms

Reference 35

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This paper cites Learned initializations for optimizing coordinate-based neural representations.

Generating visual explanations from deep networks using implicit neural representations Learned initializations for optimizing coordinate-based neural representations

Reference 36

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This paper cites Fourier features let networks learn high frequency functions in low dimen- sional domains.

Generating visual explanations from deep networks using implicit neural representations Fourier features let networks learn high frequency functions in low dimen- sional domains

Reference 37

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This paper cites A survey on explainable artificial intelligence (xai): Toward medical xai.

Generating visual explanations from deep networks using implicit neural representations A survey on explainable artificial intelligence (xai): Toward medical xai

Reference 38

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This paper cites Score-cam: Score-weighted visual explanations for convolutional neural networks.

Generating visual explanations from deep networks using implicit neural representations Score-cam: Score-weighted visual explanations for convolutional neural networks

Reference 39

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Generating visual explanations from deep networks using implicit neural representations Implicit neural representations for deformable image registration

Reference 40

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Generating visual explanations from deep networks using implicit neural representations Neural fields in visual computing and beyond

Reference 41

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This paper cites Signal processing for implicit neural rep- resentations.

Generating visual explanations from deep networks using implicit neural representations Signal processing for implicit neural rep- resentations

Reference 42

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Generating visual explanations from deep networks using implicit neural representations Geometry processing with neural fields

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This paper cites A structured dictionary perspective on implicit neural representations.

Generating visual explanations from deep networks using implicit neural representations A structured dictionary perspective on implicit neural representations

Reference 44

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This paper cites Opti-CAM: Optimizing saliency maps for interpretability.

Generating visual explanations from deep networks using implicit neural representations Opti-CAM: Optimizing saliency maps for interpretability

Reference 45

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This paper cites Learning deep features for discrimina- tive localization.

Generating visual explanations from deep networks using implicit neural representations Learning deep features for discrimina- tive localization

Reference 46

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