Paper Citation Record · LEDGER

Training-Free Restoration of Pruned Neural Networks

As of 17 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2502.08474.

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

pith.paper-citation-record.v1
2502.08474 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:48:56.610102Z

measured 52 of 52 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:49:04.044217Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T15:49:04.335099Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8b49a0b1-b54d-4299-962a-98c4c0f75169 · outbound

This paper cites Coreset-based neural network compression,.

Training-Free Restoration of Pruned Neural Networks Coreset-based neural network compression,

Reference 1

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Observation 6adc9d03-1708-4411-bb19-edd404419d41 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

Training-Free Restoration of Pruned Neural Networks The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:48:57.107276Z

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Observation 484129c1-81b8-415e-b2db-4f2169ee31fd · outbound

This paper cites Learning both weights and connections for efficient neural network,.

Training-Free Restoration of Pruned Neural Networks Learning both weights and connections for efficient neural network,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:48:57.098673Z

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Observation 1cefb46e-db18-4a87-b60e-408b34a43bb8 · outbound

This paper cites Snip: single-shot network pruning based on connection sensitivity,.

Training-Free Restoration of Pruned Neural Networks Snip: single-shot network pruning based on connection sensitivity,

Reference 4

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Observation 1832a356-bd6b-452b-84a7-8c58ec6f132a · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow,.

Training-Free Restoration of Pruned Neural Networks Pruning neural networks without any data by iteratively conserving synaptic flow,

Reference 5

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Observation 63c952c7-bed6-40ff-ad24-517331b834c7 · outbound

This paper cites Learning to prune deep neural networks via layer-wise optimal brain surgeon,.

Training-Free Restoration of Pruned Neural Networks Learning to prune deep neural networks via layer-wise optimal brain surgeon,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:48:57.074300Z

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Observation 5daf189a-7129-452b-ad93-9e84a9f8a877 · outbound

This paper cites Extremely sparse networks via binary augmented pruning for fast image classification,.

Training-Free Restoration of Pruned Neural Networks Extremely sparse networks via binary augmented pruning for fast image classification,

Reference 7

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Observation e9559110-267b-4f23-afd7-c84ae5cb78dc · outbound

This paper cites Filter pruning via geometric median for deep convolutional neural networks acceleration,.

Training-Free Restoration of Pruned Neural Networks Filter pruning via geometric median for deep convolutional neural networks acceleration,

Reference 8

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Observation d707b6ca-cfb6-4e36-8a2e-9044ad774bd9 · outbound

This paper cites Channel pruning for accelerating very deep neural networks,.

Training-Free Restoration of Pruned Neural Networks Channel pruning for accelerating very deep neural networks,

Reference 9

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Observation 132ce426-eced-432a-a305-ddc511a7da57 · outbound

This paper cites Eagleeye: Fast sub-net evaluation for efficient neural network pruning,.

Training-Free Restoration of Pruned Neural Networks Eagleeye: Fast sub-net evaluation for efficient neural network pruning,

Reference 10

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Observation 8bfb7186-9f9c-485a-b074-cd2bff7f7d74 · outbound

This paper cites Rethinking the value of network pruning,.

Training-Free Restoration of Pruned Neural Networks Rethinking the value of network pruning,

Reference 11

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Observation 22d7f81e-f2b4-427d-bac4-be55b2dffa31 · outbound

This paper cites Thinet: A filter level pruning method for deep neural network compression,.

Training-Free Restoration of Pruned Neural Networks Thinet: A filter level pruning method for deep neural network compression,

Reference 12

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Observation c6a0d312-e6f0-49af-83e6-3ec39672accf · outbound

This paper cites Discrimination-aware channel pruning for deep neural networks,.

Training-Free Restoration of Pruned Neural Networks Discrimination-aware channel pruning for deep neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:48:57.026215Z

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Observation 24a09424-a2a1-4419-bc4b-e54841fd3b93 · outbound

This paper cites Soft filter pruning for accelerating deep convolutional neural networks,.

Training-Free Restoration of Pruned Neural Networks Soft filter pruning for accelerating deep convolutional neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:48:57.018096Z

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Observation b5765af2-626f-4e09-8efb-fcd539f19f8a · outbound

This paper cites Accelerating convolutional networks via global & dynamic filter pruning,.

Training-Free Restoration of Pruned Neural Networks Accelerating convolutional networks via global & dynamic filter pruning,

Reference 15

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Observation daa923a2-4bcf-4aa1-accb-eb100c6636c2 · outbound

This paper cites Neural network pruning with residual-connections and limited-data,.

Training-Free Restoration of Pruned Neural Networks Neural network pruning with residual-connections and limited-data,

Reference 16

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Observation 59b3426a-35aa-4a15-8428-8dca6b0c7b83 · outbound

This paper cites Importance estimation for neural network pruning,.

Training-Free Restoration of Pruned Neural Networks Importance estimation for neural network pruning,

Reference 17

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Observation 4417fa6b-5dd3-4e78-a6b6-61a63ee05651 · outbound

This paper cites NISP: pruning networks using neuron importance score propagation,.

Training-Free Restoration of Pruned Neural Networks NISP: pruning networks using neuron importance score propagation,

Reference 18

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Observation cdcb569d-f4bb-49bb-9b5a-02b66623b02a · outbound

This paper cites Exploring the limits of weakly supervised pretraining,.

Training-Free Restoration of Pruned Neural Networks Exploring the limits of weakly supervised pretraining,

Reference 19

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Observation a554605c-6ce8-4029-9ee4-d1d97144c683 · outbound

This paper cites Reborn filters: Pruning convolutional neural networks with limited data,.

Training-Free Restoration of Pruned Neural Networks Reborn filters: Pruning convolutional neural networks with limited data,

Reference 20

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Observation 2e8facaa-a5d8-4ce5-ac8d-92bb8a9c4f69 · outbound

This paper cites Neuron merging: Compensating for pruned neurons,.

Training-Free Restoration of Pruned Neural Networks Neuron merging: Compensating for pruned neurons,

Reference 21

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Observation 8b10310d-0707-47d4-a919-dfcea48307f4 · outbound

This paper cites Data-free parameter pruning for deep neural networks,.

Training-Free Restoration of Pruned Neural Networks Data-free parameter pruning for deep neural networks,

Reference 22

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Observation e0d5883e-3cdc-4d3f-926c-fd3eb0ec2764 · outbound

This paper cites Deep residual learning for image recognition,.

Training-Free Restoration of Pruned Neural Networks Deep residual learning for image recognition,

Reference 23

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Observation a1d9a190-d19e-45f1-ac3a-a237df7a2aee · outbound

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

Training-Free Restoration of Pruned Neural Networks Imagenet: A large-scale hierarchical image database,

Reference 24

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Observation 61f9adee-f76f-4224-a193-28db74cb2eda · outbound

This paper cites Adaptive filter pruning via sensitivity feedback,.

Training-Free Restoration of Pruned Neural Networks Adaptive filter pruning via sensitivity feedback,

Reference 25

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Observation b5f7a712-d909-4e03-bb11-f50ac9867c9e · outbound

This paper cites Towards efficient model compression via learned global ranking,.

Training-Free Restoration of Pruned Neural Networks Towards efficient model compression via learned global ranking,

Reference 26

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Observation 9e91d090-4350-42aa-b4bc-b68c29339dff · outbound

This paper cites Hierarchical threshold pruning based on uniform response criterion,.

Training-Free Restoration of Pruned Neural Networks Hierarchical threshold pruning based on uniform response criterion,

Reference 27

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Observation 008f5322-ee14-4e6d-a3b0-0b2c084502d9 · outbound

This paper cites CATRO: channel pruning via class-aware trace ratio optimization,.

Training-Free Restoration of Pruned Neural Networks CATRO: channel pruning via class-aware trace ratio optimization,

Reference 28

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Observation 973f4478-7973-4e41-bc41-de22b1072eb4 · outbound

This paper cites Data- independent neural pruning via coresets,.

Training-Free Restoration of Pruned Neural Networks Data- independent neural pruning via coresets,

Reference 29

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Observation 467489ec-d9f4-407b-827d-7776757658e0 · outbound

This paper cites Fast filter pruning via coarse-to-fine neural architecture search and contrastive knowledge transfer,.

Training-Free Restoration of Pruned Neural Networks Fast filter pruning via coarse-to-fine neural architecture search and contrastive knowledge transfer,

Reference 30

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Observation 957a8609-eea1-47a9-a7fa-274a0a6e4211 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Training-Free Restoration of Pruned Neural Networks Distilling the Knowledge in a Neural Network

Reference 31

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This paper cites RED++ : Data-free pruning of deep neural networks via input splitting and output merging,.

Training-Free Restoration of Pruned Neural Networks RED++ : Data-free pruning of deep neural networks via input splitting and output merging,

Reference 32

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Observation 94f39705-59e6-410e-a472-812441d5b595 · outbound

This paper cites Knowledge extraction with no observable data,.

Training-Free Restoration of Pruned Neural Networks Knowledge extraction with no observable data,

Reference 33

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Observation e3c8d235-81e0-4619-bb94-d291e4763c10 · outbound

This paper cites Data-free learning of student networks,.

Training-Free Restoration of Pruned Neural Networks Data-free learning of student networks,

Reference 34

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Observation 7ca7a9b9-f1f1-4da9-8690-e8f229992892 · outbound

This paper cites Zero-shot knowledge transfer via adversar- ial belief matching,.

Training-Free Restoration of Pruned Neural Networks Zero-shot knowledge transfer via adversar- ial belief matching,

Reference 35

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Observation 39119898-6b0e-421d-8184-98c8b48b921d · outbound

This paper cites Data-Free Adversarial Distillation.

Training-Free Restoration of Pruned Neural Networks Data-Free Adversarial Distillation

Reference 36

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Observation e2bab72c-d77a-4a6f-afbf-701cb89fdcd3 · outbound

This paper cites Dreaming to distill: Data-free knowledge transfer via deepinversion,.

Training-Free Restoration of Pruned Neural Networks Dreaming to distill: Data-free knowledge transfer via deepinversion,

Reference 37

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Observation 5530b5fe-c0fe-481c-9e9a-14291dc6a632 · outbound

This paper cites Data-free network pruning for model compression,.

Training-Free Restoration of Pruned Neural Networks Data-free network pruning for model compression,

Reference 38

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Observation 0861be33-c3ad-4750-b9ad-ac7bbd1c40e0 · outbound

This paper cites Tensor decompositions and applications,.

Training-Free Restoration of Pruned Neural Networks Tensor decompositions and applications,

Reference 39

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Observation 80fc9f9d-8f34-45b5-aa57-48d5dd535232 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Training-Free Restoration of Pruned Neural Networks Gradient-based learning applied to document recognition,

Reference 40

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This paper cites Pytorch: An imperative style, high- performance deep learning library,.

Training-Free Restoration of Pruned Neural Networks Pytorch: An imperative style, high- performance deep learning library,

Reference 41

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Observation d8d2a0aa-cc8e-42c3-a969-fcc8f65838e4 · outbound

This paper cites Pruning filters for efficient convnets,.

Training-Free Restoration of Pruned Neural Networks Pruning filters for efficient convnets,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:48:56.794607Z

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Observation 44900dd7-48a5-4572-9a3b-174530232107 · outbound

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

Training-Free Restoration of Pruned Neural Networks Learning multiple layers of features from tiny images,

Reference 43

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Observation 352d8ebb-926a-4223-8d04-815411aa1439 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Training-Free Restoration of Pruned Neural Networks Very deep convolutional networks for large-scale image recognition,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:48:56.779064Z

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Observation 272b2488-9a92-40a2-a526-164057dd924e · outbound

This paper cites Microsoft COCO: common objects in context,.

Training-Free Restoration of Pruned Neural Networks Microsoft COCO: common objects in context,

Reference 45

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Observation 04ef270f-3043-475a-b59f-b5908ebbec3c · outbound

This paper cites SSD: single shot multibox detector,.

Training-Free Restoration of Pruned Neural Networks SSD: single shot multibox detector,

Reference 46

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Observation 403610ce-de4d-4952-8961-f7a9fe3f9f2e · outbound

This paper cites If there is only batch normalization between a feature map and its activation map, A(ℓ) = N (Z(ℓ)).

Training-Free Restoration of Pruned Neural Networks If there is only batch normalization between a feature map and its activation map, A(ℓ) = N (Z(ℓ))

Reference 48

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Observation 2245b02c-661e-4ddb-87de-30698162a6e1 · outbound

This paper cites If there are both batch normalization and a ReLU function between a feature map and its activation map, A(ℓ) = F (N (Z(ℓ))).

Training-Free Restoration of Pruned Neural Networks If there are both batch normalization and a ReLU function between a feature map and its activation map, A(ℓ) = F (N (Z(ℓ)))

Reference 49

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Observation d7197475-7c77-4892-b753-8a7cf505b712 · outbound

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Training-Free Restoration of Pruned Neural Networks Unresolved cited work

Reference 50

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Observation 3541fac9-c354-4e6d-9bcd-51c1118bbfa6 · outbound

This paper cites Our loss function is as follows.

Training-Free Restoration of Pruned Neural Networks Our loss function is as follows

Reference 51

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Observation a3503521-8333-4875-a0de-4e32dd4e4fc3 · outbound

This paper cites 1455–1464.

Training-Free Restoration of Pruned Neural Networks 1455–1464

Reference 2020

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Pith citing papers

Observation 98671b65-f957-43ba-b178-98070c9b4e6b · inbound

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression cites this paper.

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression Training-Free Restoration of Pruned Neural Networks

Reference 31

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