Paper Citation Record · LEDGER

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2412.14509.

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

pith.paper-citation-record.v1
2412.14509 v1

Coverage vector

measured 41 of 41 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 4b09f618-c975-4db6-b2f8-2450121e9aea · outbound

This paper cites Slic superpixels compared to state-of-the-art superpixel methods.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Slic superpixels compared to state-of-the-art superpixel methods

Reference 1

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Observation 30dff47a-7c11-4df5-9630-271c6764d06e · outbound

This paper cites Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.796439Z

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Observation 3fb9af3b-d292-4747-a948-6adc4213e19d · outbound

This paper cites Evaluating and Aggregating Feature-based Model Explanations.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Evaluating and Aggregating Feature-based Model Explanations

Reference 3

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Observation 493eaf12-38a4-4185-8aab-6776cd463463 · outbound

This paper cites Deep- learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Deep- learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.779202Z

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Observation 7f478b29-7062-4146-9c7f-fe6c96da74aa · outbound

This paper cites Stability and generalization.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Stability and generalization

Reference 5

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Observation 0f43414e-0ec4-41d5-9c95-a81aa8448d20 · outbound

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

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Grad-cam++: Generalized gradient-based visual explanations for deep convolu- tional networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.739814Z

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Observation b7f87c78-42a8-470c-bc5b-0e243b84e93f · outbound

This paper cites How good is your ex- planation? algorithmic stability measures to assess the quality of explanations for deep neural networks.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks How good is your ex- planation? algorithmic stability measures to assess the quality of explanations for deep neural networks

Reference 7

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Observation 80dcd618-5eee-4c7e-9744-00a1c26c99b1 · outbound

This paper cites Efficient belief propagation for early vision.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Efficient belief propagation for early vision

Reference 8

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Observation 65951b27-1dfd-4ca3-bdac-42487f1d2f80 · outbound

This paper cites Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena

Reference 9

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Observation 16de3da5-d9e1-4c6a-9014-a3417f1e3b2b · outbound

This paper cites Diffusion model based semi-supervised learning on brain hemorrhage images for efficient midline shift quantification.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Diffusion model based semi-supervised learning on brain hemorrhage images for efficient midline shift quantification

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.669065Z

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Observation bfd2333f-2df9-48c6-84a9-cc016ca8cb31 · outbound

This paper cites 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation

Reference 11

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Observation 9c62e9d6-82ce-4d3c-8142-b6d36665d8e9 · outbound

This paper cites Structured gradient-based interpretations via norm-regularized adversarial training.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Structured gradient-based interpretations via norm-regularized adversarial training

Reference 12

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Observation 5a1a48d8-d972-42b5-9e41-199f947b32c4 · outbound

This paper cites Collection-cam: A faster region-based saliency method using collection-wise mask over pyramidal features.IEEE Access, 10:112776– 112788, 2022.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Collection-cam: A faster region-based saliency method using collection-wise mask over pyramidal features.IEEE Access, 10:112776– 112788, 2022

Reference 13

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Observation 73acb53f-9182-4dfc-8a76-0f131fe747c4 · outbound

This paper cites Deep residual learning for image recognition.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Deep residual learning for image recognition

Reference 14

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Observation 3a15ce39-0f51-4001-906f-cd4cb8c786ce · outbound

This paper cites A Benchmark for Interpretability Methods in Deep Neural Networks.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 15

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Observation 3d53ee90-c025-4271-924d-02b93c808d94 · outbound

This paper cites Directional convergence and alignment in deep learning.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Directional convergence and alignment in deep learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.588482Z

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Observation e692b691-4e86-443c-8600-be1366463259 · outbound

This paper cites Transdose: Transformer-based radiotherapy dose prediction from ct images guided by super-pixel-level gcn classification.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Transdose: Transformer-based radiotherapy dose prediction from ct images guided by super-pixel-level gcn classification

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.562252Z

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Observation 50757d02-1452-4de8-aabb-bb27aa67cab5 · outbound

This paper cites Xrai: Bet- ter attributions through regions.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Xrai: Bet- ter attributions through regions

Reference 18

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Observation 6527589a-5b2d-4286-b1c7-7e31d07a50bb · outbound

This paper cites Why are saliency maps noisy? cause of and solution to noisy saliency maps.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Why are saliency maps noisy? cause of and solution to noisy saliency maps

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.515498Z

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Observation 684cd976-6f26-485b-8f67-0c6f06ee3083 · outbound

This paper cites Interpretable learning for self-driving cars by visualizing causal attention.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Interpretable learning for self-driving cars by visualizing causal attention

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.491810Z

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Observation 21c703da-b79d-450a-8675-0625eca8cf21 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Imagenet classification with deep convolutional neural networks

Reference 21

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Observation 85169e02-0ddc-475d-a01a-1889d795e690 · outbound

This paper cites Certifiably Robust Interpretation in Deep Learning.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Certifiably Robust Interpretation in Deep Learning

Reference 22

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Observation e8d8d4c2-9d12-4710-a5ba-a6f6fc2ea3b4 · outbound

This paper cites Super- pixel guided low-light images enhancement with features restoration.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Super- pixel guided low-light images enhancement with features restoration

Reference 23

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Observation 39ce57b7-c984-4444-85a4-28c2b80ccffb · outbound

This paper cites Detection of anaemia from retinal fundus images via deep learning.Nature Biomedical Engineering, 4(1):18–27, 2020.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Detection of anaemia from retinal fundus images via deep learning.Nature Biomedical Engineering, 4(1):18–27, 2020

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.432398Z

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Observation 31443687-1809-4df3-94aa-258845febfde · outbound

This paper cites Compact watershed and preemptive slic: On improving trade-offs of superpixel segmentation algorithms.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Compact watershed and preemptive slic: On improving trade-offs of superpixel segmentation algorithms

Reference 25

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Observation 1081109d-d2e4-4ba5-8a32-209db1273e3b · outbound

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

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 26

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Observation a5fbade5-b356-47d0-a862-e9bb56393b1d · outbound

This paper cites A Consistent and Efficient Evaluation Strategy for Attribution Methods.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks A Consistent and Efficient Evaluation Strategy for Attribution Methods

Reference 27

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

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 28

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This paper cites Learning important features through propagating activation differences.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Learning important features through propagating activation differences

Reference 29

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Observation 9797efe5-b65a-4f9f-bc10-67c32ddc48e5 · outbound

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

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

Reference 30

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Observation 884f2a9d-2dd6-4e1e-b06e-426b9fdc208a · outbound

This paper cites SmoothGrad: removing noise by adding noise.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks SmoothGrad: removing noise by adding noise

Reference 31

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Observation fa0744b8-aa8b-4ab3-b0b4-00e621ed82a3 · outbound

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

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Striving for Simplicity: The All Convolutional Net

Reference 32

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Observation 35b7ed89-c930-4c86-b13e-a9a299c13271 · outbound

This paper cites Axiomatic attribution for deep net- works.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Axiomatic attribution for deep net- works

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.359362Z

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Observation f7f9b9b1-c580-4348-ba08-ab9c5d18ad61 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 34

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Observation b01ef57f-cecc-4988-8e77-9a1f61d1ab00 · outbound

This paper cites Quick shift and kernel methods for mode seek- ing.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Quick shift and kernel methods for mode seek- ing

Reference 35

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Observation 5b66f2d7-d766-400f-bc63-94eb578d2bd3 · outbound

This paper cites Dynamic super-pixel normal- ization for robust hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–13, 2023.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Dynamic super-pixel normal- ization for robust hyperspectral image classification.IEEE Transactions on Geoscience and Remote Sensing, 61:1–13, 2023

Reference 36

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Observation e8dc7c06-166f-4e06-95d2-5915ea278b1f · outbound

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

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Score-cam: Score-weighted visual explanations for convo- lutional neural networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.280449Z

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Observation e6fed58d-0ed6-4c15-bc47-0701966e0bd1 · outbound

This paper cites Initialization noise in image gradients and saliency maps.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Initialization noise in image gradients and saliency maps

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.254040Z

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Observation 3c67da99-d880-4153-9c75-8fa476de417d · outbound

This paper cites Visualizing and understanding convolutional net- works.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Visualizing and understanding convolutional net- works

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:14:32.234640Z

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Observation 7b972071-2a76-46de-a52b-137c69ebc7fd · outbound

This paper cites MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-11T12:14:31.925125Z

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Observation f4a5a997-7369-44dd-a6da-39d3a926bb8d · outbound

This paper cites Object detection with deep learning: A review.

A Super-pixel-based Approach to the Stable Interpretation of Neural Networks Object detection with deep learning: A review

Reference 41

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Pith citing papers

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