Paper Citation Record · LEDGER

CASE: Contrastive Activation for Saliency Estimation

As of 17 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.07327.

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

pith.paper-citation-record.v1
2506.07327 v3

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:42:14.343374Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

31 of 31 outbound references displayed

verified exact2
verified fuzzy3
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 59c99200-f176-4ef7-86c9-757a8d0dd4e9 · outbound

This paper cites Sanity checks for saliency maps.

CASE: Contrastive Activation for Saliency Estimation Sanity checks for saliency maps

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:42:15.286595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1c403720-e528-40a6-923f-8a4c3a6fafbb · outbound

This paper cites Evaluating saliency map explanations for convolutional neural networks: a user study.

CASE: Contrastive Activation for Saliency Estimation Evaluating saliency map explanations for convolutional neural networks: a user study

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.214964Z

Source-reported events for the cited work

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Observation 3558c1cc-c6f9-4757-9809-66ac5fbeaf79 · outbound

This paper cites On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.

CASE: Contrastive Activation for Saliency Estimation On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.219841Z

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Observation b88d6060-ecfa-4ef7-951f-1356fa24b89c · outbound

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

CASE: Contrastive Activation for Saliency Estimation Network Dissection: Quantifying Interpretability of Deep Visual Representations

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.224529Z

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Observation 770d8bd9-3b40-41c3-987e-7d32c9441eaa · outbound

This paper cites Explaining Image Classifiers by Counterfactual Generation.

CASE: Contrastive Activation for Saliency Estimation Explaining Image Classifiers by Counterfactual Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.229315Z

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Observation c64758db-1e70-4bb0-9bc4-4c53313c0eff · outbound

This paper cites Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks.

CASE: Contrastive Activation for Saliency Estimation Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.234017Z

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Observation 69b21113-bbd5-41da-9bef-da8a27210004 · outbound

This paper cites Latent Tree Models for Hierarchical Topic Detection.

CASE: Contrastive Activation for Saliency Estimation Latent Tree Models for Hierarchical Topic Detection

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:42:15.044456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b51d5c4-5b3e-458a-95b9-5a6a93fac3c3 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

CASE: Contrastive Activation for Saliency Estimation Imagenet: A large-scale hierarchical image database

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.243539Z

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Observation d6bb8ddf-8de1-43cc-ab51-6581df3bb4e3 · outbound

This paper cites Ramaswamy.

CASE: Contrastive Activation for Saliency Estimation Ramaswamy

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.247604Z

Source-reported events for the cited work

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Observation fda9e4ea-2437-4d9e-a7b8-a7d5eed9533e · outbound

This paper cites Understanding Individual Decisions of CNNs via Contrastive Backpropagation.

CASE: Contrastive Activation for Saliency Estimation Understanding Individual Decisions of CNNs via Contrastive Backpropagation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.251904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 807f16dc-c163-487c-8b28-44d7f8e56f30 · outbound

This paper cites Deep Residual Learning for Image Recognition.

CASE: Contrastive Activation for Saliency Estimation Deep Residual Learning for Image Recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.256961Z

Source-reported events for the cited work

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Observation d296f4ce-050c-4458-8f61-09e4938dacb1 · outbound

This paper cites A benchmark for interpretability methods in deep neural networks.

CASE: Contrastive Activation for Saliency Estimation A benchmark for interpretability methods in deep neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:42:15.271976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f78d63e2-d978-4031-a8a6-aa63069ea291 · outbound

This paper cites Densely Connected Convolutional Networks.

CASE: Contrastive Activation for Saliency Estimation Densely Connected Convolutional Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.265337Z

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Observation 62c5877d-3868-4416-82b2-0a031aa73028 · outbound

This paper cites CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency.

CASE: Contrastive Activation for Saliency Estimation CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:42:14.808895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b08cd42-0b6d-4129-8d88-def5510cc0bd · outbound

This paper cites Layercam: Exploring hierarchical class activation maps for localization.

CASE: Contrastive Activation for Saliency Estimation Layercam: Exploring hierarchical class activation maps for localization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.274274Z

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Unavailable: canonical work link unavailable.

Observation f6948f1b-04f9-4e31-b370-a98b54c7f5d1 · outbound

This paper cites Measuring self-supervised representation quality for downstream classification using discriminative features, 2023.

CASE: Contrastive Activation for Saliency Estimation Measuring self-supervised representation quality for downstream classification using discriminative features, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:42:15.256605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 68eb4e53-61b1-47c4-8af1-80676c280335 · outbound

This paper cites HIVE: Evaluating the Human Interpretability of Visual Explanations.

CASE: Contrastive Activation for Saliency Estimation HIVE: Evaluating the Human Interpretability of Visual Explanations

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:42:14.712486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7f91b1f7-9335-41fb-a897-3ea38f3a93b2 · outbound

This paper cites The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.

CASE: Contrastive Activation for Saliency Estimation The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.286693Z

Source-reported events for the cited work

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Observation 8c785740-b134-461c-aab6-87f3215c93e8 · outbound

This paper cites Learning multiple layers of features from tiny images.

CASE: Contrastive Activation for Saliency Estimation Learning multiple layers of features from tiny images

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.291009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3507e5fc-bdd7-41d4-b881-6dc49922e173 · outbound

This paper cites An Evaluation of the Human-Interpretability of Explanation.

CASE: Contrastive Activation for Saliency Estimation An Evaluation of the Human-Interpretability of Explanation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.294998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f5f4e03-595c-4ed2-80a3-d9a54546934b · outbound

This paper cites Faithful and customizable explanations of black box models.

CASE: Contrastive Activation for Saliency Estimation Faithful and customizable explanations of black box models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.299865Z

Source-reported events for the cited work

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Observation b5f2145f-e440-45f8-8c5e-b66943120abd · outbound

This paper cites A ConvNet for the 2020s.

CASE: Contrastive Activation for Saliency Estimation A ConvNet for the 2020s

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.303924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a53c426-f725-4377-9668-cccff4dea0e5 · outbound

This paper cites an unresolved cited work.

CASE: Contrastive Activation for Saliency Estimation Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.308085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5dc29210-80f9-4d6c-ab55-31a2b913ca70 · outbound

This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

CASE: Contrastive Activation for Saliency Estimation RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.312248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9585c947-8079-4292-aa67-0b1da99e281b · outbound

This paper cites Evaluating the visualization of what a Deep Neural Network has learned.

CASE: Contrastive Activation for Saliency Estimation Evaluating the visualization of what a Deep Neural Network has learned

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.316454Z

Source-reported events for the cited work

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Observation b7381f7c-2b16-469e-9cc3-ca221504c879 · outbound

This paper cites Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.

CASE: Contrastive Activation for Saliency Estimation Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.320562Z

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Observation d379f320-5361-42c5-9eed-3a66b3e68b18 · outbound

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

CASE: Contrastive Activation for Saliency Estimation A survey on explainable artificial intelligence (xai): Toward medical xai

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.326461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0f304da-df27-4811-a798-b871190801a5 · outbound

This paper cites Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks.

CASE: Contrastive Activation for Saliency Estimation Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.330813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66c7899e-84ed-4db0-81c3-60d2a549e489 · outbound

This paper cites an unresolved cited work.

CASE: Contrastive Activation for Saliency Estimation Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:42:15.228739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8eedd191-3a96-40ab-baa6-2bd611f15594 · outbound

This paper cites On the (In)fidelity and Sensitivity for Explanations.

CASE: Contrastive Activation for Saliency Estimation On the (In)fidelity and Sensitivity for Explanations

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.338981Z

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Observation 2ff31493-2496-46e7-90fc-60212ca15ff8 · outbound

This paper cites Learning Deep Features for Discriminative Localization.

CASE: Contrastive Activation for Saliency Estimation Learning Deep Features for Discriminative Localization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:14.343374Z

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Pith citing papers

No inbound Pith citation observations are available.