Paper Citation Record · LEDGER

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks

As of 6 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2512.21315.

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

pith.paper-citation-record.v1
2512.21315 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T14:13:36.054185Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 6b09afd9-e63f-4a45-b249-3aa3fa7da227 · outbound

This paper cites write newline.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks write newline

Reference 1

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This paper cites @esa (Ref.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks @esa (Ref

Reference 2

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Unresolved cited work

Reference 3

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Dz Co f^ z eOza F zެ ޳

Reference 4

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Pattern recognition and machine learning

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This paper cites Risk bounds for over-parameterized maximum margin classification on sub-gaussian mixtures.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Risk bounds for over-parameterized maximum margin classification on sub-gaussian mixtures

Reference 6

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This paper cites Smote: synthetic minority over-sampling technique.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Smote: synthetic minority over-sampling technique

Reference 7

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This paper cites Elements of information theory.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Elements of information theory

Reference 8

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This paper cites Is image super-resolution helpful for other vision tasks? In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp.\ 1--9.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Is image super-resolution helpful for other vision tasks? In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp.\ 1--9

Reference 9

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This paper cites A model of double descent for high-dimensional binary linear classification.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks A model of double descent for high-dimensional binary linear classification

Reference 10

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This paper cites An introduction to probability theory and its applications, Volume 2, volume 2.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks An introduction to probability theory and its applications, Volume 2, volume 2

Reference 11

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This paper cites Introduction to statistical pattern recognition.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Introduction to statistical pattern recognition

Reference 12

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This paper cites Entropy and mutual information in models of deep neural networks.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Entropy and mutual information in models of deep neural networks

Reference 13

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This paper cites Sliced mutual information: A scalable measure of statistical dependence.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Sliced mutual information: A scalable measure of statistical dependence

Reference 14

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This paper cites Task-driven super resolution: Object detection in low-resolution images.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Task-driven super resolution: Object detection in low-resolution images

Reference 15

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Deep residual learning for image recognition

Reference 16

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This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Benchmarking neural network robustness to common corruptions and perturbations

Reference 17

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This paper cites Learning deep representation for imbalanced classification.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Learning deep representation for imbalanced classification

Reference 18

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This paper cites An information-theoretic framework for deep learning.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks An information-theoretic framework for deep learning

Reference 19

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Observation d4ea7dad-bb18-4860-9d8a-4bbef49a751e · outbound

This paper cites Fundamentals of statistical signal processing: estimation theory.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Fundamentals of statistical signal processing: estimation theory

Reference 20

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This paper cites Can kernel methods explain how the data affects neural collapse? Transactions on Machine Learning Research, 2025.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Can kernel methods explain how the data affects neural collapse? Transactions on Machine Learning Research, 2025

Reference 21

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Learning multiple layers of features from tiny images

Reference 22

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Fifo: Learning fog-invariant features for foggy scene segmentation

Reference 23

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This paper cites Detection-friendly dehazing: Object detection in real-world hazy scenes.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Detection-friendly dehazing: Object detection in real-world hazy scenes

Reference 24

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Autobalance: Optimized loss functions for imbalanced data

Reference 25

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks When image denoising meets high-level vision tasks: A deep learning approach

Reference 26

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks On the classification-distortion-perception tradeoff

Reference 27

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This paper cites Ditch the denoiser: Emergence of noise robustness in self-supervised learning from data curriculum.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Ditch the denoiser: Emergence of noise robustness in self-supervised learning from data curriculum

Reference 28

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This paper cites Does haze removal help cnn-based image classification? In Proceedings of the European conference on computer vision (ECCV), pp.\ 682--697, 2018.

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Does haze removal help cnn-based image classification? In Proceedings of the European conference on computer vision (ECCV), pp.\ 682--697, 2018

Reference 29

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks On the information bottleneck theory of deep learning

Reference 30

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Opening the Black Box of Deep Neural Networks via Information

Reference 31

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Dual directed capsule network for very low resolution image recognition

Reference 32

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Training deep learning based denoisers without ground truth data

Reference 33

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Urie: Universal image enhancement for visual recognition in the wild

Reference 34

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Estimation of the mean of a multivariate normal distribution

Reference 35

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Deep learning and the information bottleneck principle

Reference 36

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Matching networks for one shot learning

Reference 37

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Binary classification of gaussian mixtures: Abundance of support vectors, benign overfitting, and regularization

Reference 38

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Denoising masked autoencoders help robust classification

Reference 39

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks A theory of usable information under computational constraints

Reference 40

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Enhancing the performance of convolutional neural networks on quality degraded datasets

Reference 41

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising

Reference 42

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Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks Anomaly detection with robust deep autoencoders

Reference 43

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Pith citing papers

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