Paper Citation Record · LEDGER

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

As of 12 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 2026-08-11T06:34:44.6726+00:00

measured 100 of 234 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:54:24.631850Z

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
parse uncertain0
malformed identifier0
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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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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

Reference 100

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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

Reference 87

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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

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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

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Explaining Object Detectors via Collective Contribution of Pixels cites this paper.

Explaining Object Detectors via Collective Contribution of Pixels Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey cites this paper.

Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Filterless Snapshot Hyperspectral Imaging using Guided Patch Diffusion cites this paper.

Filterless Snapshot Hyperspectral Imaging using Guided Patch Diffusion Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task cites this paper.

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Policy-shaped prediction: avoiding distractions in model-based reinforcement learning cites this paper.

Policy-shaped prediction: avoiding distractions in model-based reinforcement learning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Understanding Transformer-based Vision Models through Inversion cites this paper.

Understanding Transformer-based Vision Models through Inversion Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Language Model as Visual Explainer cites this paper.

Language Model as Visual Explainer Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Post-Hoc MOTS: Exploring the Capabilities of Time-Symmetric Multi-Object Tracking cites this paper.

Post-Hoc MOTS: Exploring the Capabilities of Time-Symmetric Multi-Object Tracking Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation cites this paper.

Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity cites this paper.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Identifying Bias in Deep Neural Networks Using Image Transforms cites this paper.

Identifying Bias in Deep Neural Networks Using Image Transforms Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future cites this paper.

A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation cites this paper.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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A Super-pixel-based Approach to the Stable Interpretation of Neural Networks cites this paper.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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LitLLMs, LLMs for Literature Review: Are we there yet? cites this paper.

LitLLMs, LLMs for Literature Review: Are we there yet? Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Can Input Attributions Explain Inductive Reasoning in In-Context Learning? cites this paper.

Can Input Attributions Explain Inductive Reasoning in In-Context Learning? Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Explaining in Diffusion: Explaining a Classifier Through Hierarchical Semantics with Text-to-Image Diffusion Models cites this paper.

Explaining in Diffusion: Explaining a Classifier Through Hierarchical Semantics with Text-to-Image Diffusion Models Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation cites this paper.

Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Developing Explainable Machine Learning Model using Augmented Concept Activation Vector cites this paper.

Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of Explainability cites this paper.

xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of Explainability Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 30

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Attribution for Enhanced Explanation with Transferable Adversarial eXploration cites this paper.

Attribution for Enhanced Explanation with Transferable Adversarial eXploration Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Human-Centered Explainability in Interactive Information Systems: A Survey cites this paper.

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Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey cites this paper.

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CircFormerMoE: An End-to-End Deep Learning Framework for Circular RNA Splice Site Detection and Pairing in Plant Genomes cites this paper.

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Human-Centered Supervision for Sentiment Analysis in Telugu: A Systematic Inquiry Beyond Accuracy cites this paper.

Human-Centered Supervision for Sentiment Analysis in Telugu: A Systematic Inquiry Beyond Accuracy Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 48

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A Survey on Data Security in Large Language Models cites this paper.

A Survey on Data Security in Large Language Models Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 59

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DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations cites this paper.

DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 40

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AttnTrace: Contextual Attribution of Prompt Injection and Knowledge Corruption cites this paper.

AttnTrace: Contextual Attribution of Prompt Injection and Knowledge Corruption Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-19T00:36:56.389240Z

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Decorrelated feature importance from local sample weighting cites this paper.

Reference 39

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On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations cites this paper.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 62

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