Paper Citation Record · LEDGER

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

As of 22 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 100 inbound Pith citation observations for arXiv:1312.6034.

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

pith.paper-citation-record.v1
1312.6034 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T18:51:37.983908Z

measured 113 of 113 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 100 of 299 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:29:39.877135Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

13 of 13 outbound references displayed

verified exact1
verified fuzzy8
unresolved4
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External citation measurements

893
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 75edcfc2-a9fa-4e2d-8d08-1a932e543af0 · outbound

This paper cites Baehrens, T.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Baehrens, T

Reference 1

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Observation 60623edc-9a2d-4c6f-a96f-efe5e6090824 · outbound

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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Unresolved cited work

Reference 2

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Observation 8c139f2e-a0a5-4a1c-8d23-373fe22e8920 · outbound

This paper cites Boykov and M.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Boykov and M

Reference 3

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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Unresolved cited work

Reference 4

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Observation 78526248-f073-4578-a32b-70eebc91c0ab · outbound

This paper cites Erhan, Y.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Erhan, Y

Reference 5

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Observation d6e02093-77e8-4714-b910-60ad8e2ae047 · outbound

This paper cites Felzenszwalb, D.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Felzenszwalb, D

Reference 6

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Observation c59a14c8-586a-4e41-88df-252b4193a17d · outbound

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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Unresolved cited work

Reference 7

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Observation eb20b486-8374-45a0-99cc-71853082133d · outbound

This paper cites Krizhevsky, I.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Krizhevsky, I

Reference 8

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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Unresolved cited work

Reference 9

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Observation dfacaf3c-8ca7-420c-92df-643b5d5225a5 · outbound

This paper cites LeCun, L.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps LeCun, L

Reference 10

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Observation 4abdfc81-129a-4737-b077-011734e658dc · outbound

This paper cites Perronnin, J.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Perronnin, J

Reference 11

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Observation dc1130ed-e2ba-4f37-9a58-1dfd77fa2631 · outbound

This paper cites Simonyan, A.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Simonyan, A

Reference 12

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Observation e8574e3a-95c1-43cb-b9bd-e238032db171 · outbound

This paper cites Visualizing and Understanding Convolutional Networks.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Visualizing and Understanding Convolutional Networks

Reference 13

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Pith citing papers

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DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems cites this paper.

DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 29

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Observation 9ef528cd-8ecd-4eac-95ae-e01d477a0a78 · inbound

Saliency-driven Word Alignment Interpretation for Neural Machine Translation cites this paper.

Saliency-driven Word Alignment Interpretation for Neural Machine Translation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 35

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Observation ce7f2a55-3a8a-4636-90d3-635a8cd78850 · inbound

Generative Counterfactual Introspection for Explainable Deep Learning cites this paper.

Generative Counterfactual Introspection for Explainable Deep Learning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 4

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Observation d17bf944-e213-4f76-ae10-10110ebefafd · inbound

ELF: Embedded Localisation of Features in pre-trained CNN cites this paper.

ELF: Embedded Localisation of Features in pre-trained CNN Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 35

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Observation 7ec204a4-777c-481a-a013-e379b5e9ef15 · inbound

Unsupervised Domain Alignment to Mitigate Low Level Dataset Biases cites this paper.

Unsupervised Domain Alignment to Mitigate Low Level Dataset Biases Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 35

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Explaining an increase in predicted risk for clinical alerts cites this paper.

Explaining an increase in predicted risk for clinical alerts Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 8

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Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications cites this paper.

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 32

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Unsupervised Machine Learning to Teach Fluid Dynamicists to Think in 15 Dimensions cites this paper.

Unsupervised Machine Learning to Teach Fluid Dynamicists to Think in 15 Dimensions Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 42

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Observation 36c2e690-e4e1-4436-a094-d6bb06fed2cc · inbound

Interpretability Beyond Classification Output: Semantic Bottleneck Networks cites this paper.

Interpretability Beyond Classification Output: Semantic Bottleneck Networks Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 23

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Automatically Learning Construction Injury Precursors from Text cites this paper.

Automatically Learning Construction Injury Precursors from Text Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 61

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Multi-task Self-Supervised Learning for Human Activity Detection cites this paper.

Multi-task Self-Supervised Learning for Human Activity Detection Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 63

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Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models cites this paper.

Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition cites this paper.

Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems cites this paper.

TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Free-Lunch Saliency via Attention in Atari Agents cites this paper.

Free-Lunch Saliency via Attention in Atari Agents Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation cites this paper.

Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning cites this paper.

NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Implicit Deep Learning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Scalable Explanation of Inferences on Large Graphs cites this paper.

Scalable Explanation of Inferences on Large Graphs Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods cites this paper.

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization cites this paper.

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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SROBB: Targeted Perceptual Loss for Single Image Super-Resolution cites this paper.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Neural Image Compression and Explanation cites this paper.

Neural Image Compression and Explanation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Gradient Weighted Superpixels for Interpretability in CNNs cites this paper.

Gradient Weighted Superpixels for Interpretability in CNNs Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Facial age estimation by deep residual decision making cites this paper.

Facial age estimation by deep residual decision making Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective cites this paper.

Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial cites this paper.

Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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LCA: Loss Change Allocation for Neural Network Training cites this paper.

LCA: Loss Change Allocation for Neural Network Training Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Explaining the Explainers in Graph Neural Networks: a Comparative Study cites this paper.

Explaining the Explainers in Graph Neural Networks: a Comparative Study Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation cites this paper.

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning cites this paper.

Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Improving Dictionary Learning with Gated Sparse Autoencoders cites this paper.

Improving Dictionary Learning with Gated Sparse Autoencoders Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Scaling and evaluating sparse autoencoders cites this paper.

Scaling and evaluating sparse autoencoders Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Explaining Graph Neural Networks for Node Similarity on Graphs cites this paper.

Explaining Graph Neural Networks for Node Similarity on Graphs Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 69

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xAI-Drop: Don't Use What You Cannot Explain cites this paper.

xAI-Drop: Don't Use What You Cannot Explain Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 49

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Fill in the blanks: Rethinking Interpretability in vision cites this paper.

Fill in the blanks: Rethinking Interpretability in vision Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models cites this paper.

DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data cites this paper.

Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Stable Flow: Vital Layers for Training-Free Image Editing cites this paper.

Stable Flow: Vital Layers for Training-Free Image Editing Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 78

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Predicting Halo Formation Time Using Machine Learning cites this paper.

Predicting Halo Formation Time Using Machine Learning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 93

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Application of Sensitivity Analysis Methods for Studying Neural Network Models cites this paper.

Application of Sensitivity Analysis Methods for Studying Neural Network Models Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 28

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Explainable Unsupervised Anomaly Detection with Random Forest cites this paper.

Explainable Unsupervised Anomaly Detection with Random Forest Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 42

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Improving Human-Autonomous Vehicle Interaction in Complex Systems cites this paper.

Improving Human-Autonomous Vehicle Interaction in Complex Systems Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 203

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