Paper Citation Record · LEDGER

Zero-shot Quantization: A Comprehensive Survey

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2505.09188.

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

pith.paper-citation-record.v1
2505.09188 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

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measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T14:14:04.500656Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T14:14:12.301041Z

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy39
unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 40cc618d-9146-4537-a04b-a6f759c6f8e7 · outbound

This paper cites Unified data-free compression: Pruning and quantization without fine-tuning.

Zero-shot Quantization: A Comprehensive Survey Unified data-free compression: Pruning and quantization without fine-tuning

Reference 1

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Observation c103db07-c0bf-4731-977f-fcb62106a65d · outbound

This paper cites Pea-kd: Parameter-efficient and accurate knowledge distillation on bert.

Zero-shot Quantization: A Comprehensive Survey Pea-kd: Parameter-efficient and accurate knowledge distillation on bert

Reference 5

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Observation 2f17660b-a641-458f-8fde-442c3ac35c89 · outbound

This paper cites an unresolved cited work.

Zero-shot Quantization: A Comprehensive Survey Unresolved cited work

Reference 8

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Observation 8b139234-4e7c-40d6-9092-eed7eee2a7f0 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Zero-shot Quantization: A Comprehensive Survey An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 10

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Observation 20bc8f11-964d-4a0d-9060-e8b9cc48e6ba · outbound

This paper cites Sharpness-aware data generation for zero-shot quantization.

Zero-shot Quantization: A Comprehensive Survey Sharpness-aware data generation for zero-shot quantization

Reference 11

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Observation b80d7951-7959-4c39-a987-97aa309e5bf7 · outbound

This paper cites Data-free quantization via pseudo-label fil- tering.

Zero-shot Quantization: A Comprehensive Survey Data-free quantization via pseudo-label fil- tering

Reference 12

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Observation 58d962e6-d990-4f91-8194-87b60f82ccc7 · outbound

This paper cites A survey of quantization methods for efficient neural network inference.

Zero-shot Quantization: A Comprehensive Survey A survey of quantization methods for efficient neural network inference

Reference 13

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Observation bc91206c-81a3-409a-877d-ccfe8e6b98fa · outbound

This paper cites Gen- erative adversarial nets.

Zero-shot Quantization: A Comprehensive Survey Gen- erative adversarial nets

Reference 14

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Observation 01beae25-2c19-4ac6-aad4-b76707bfe5e9 · outbound

This paper cites Deep learning with limited numerical precision.

Zero-shot Quantization: A Comprehensive Survey Deep learning with limited numerical precision

Reference 16

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Observation 1c3b25e8-98df-4a8b-9cda-de8c6e756a6a · outbound

This paper cites Structured pruning for deep convolutional neural networks: A survey.

Zero-shot Quantization: A Comprehensive Survey Structured pruning for deep convolutional neural networks: A survey

Reference 18

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Observation 23bedd26-7ca2-4031-b56d-a7242133d9a6 · outbound

This paper cites Deep residual learning for image recog- nition.

Zero-shot Quantization: A Comprehensive Survey Deep residual learning for image recog- nition

Reference 19

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Observation 62f22cdf-3b24-435b-bd5e-3484315a989b · outbound

This paper cites Falcon: lightweight and accurate convo- lution based on depthwise separable convolution.

Zero-shot Quantization: A Comprehensive Survey Falcon: lightweight and accurate convo- lution based on depthwise separable convolution

Reference 21

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Zero-shot Quantization: A Comprehensive Survey Unresolved cited work

Reference 22

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Observation 12b57558-c2b9-444b-b0f1-ee29b74e5f4f · outbound

This paper cites Patch similarity aware data-free quantization for vision transformers.

Zero-shot Quantization: A Comprehensive Survey Patch similarity aware data-free quantization for vision transformers

Reference 24

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Observation 7010bb9a-0edc-4bae-b566-8a46d38a1cb1 · outbound

This paper cites Hard sample matters a lot in zero-shot quan- tization.

Zero-shot Quantization: A Comprehensive Survey Hard sample matters a lot in zero-shot quan- tization

Reference 25

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Observation de8ee3f6-fa5d-4e48-8c99-ba82fdaf4108 · outbound

This paper cites Zero-shot adversarial quantization.

Zero-shot Quantization: A Comprehensive Survey Zero-shot adversarial quantization

Reference 26

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Observation df9402e6-bdcd-4c18-bd6c-21f9d927a69e · outbound

This paper cites Visual instruction tuning.

Zero-shot Quantization: A Comprehensive Survey Visual instruction tuning

Reference 27

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Observation 09b5f74f-03b4-4ffa-bcde-fe2b0b06118f · outbound

This paper cites Data-free quantization through weight equalization and bias correction.

Zero-shot Quantization: A Comprehensive Survey Data-free quantization through weight equalization and bias correction

Reference 28

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Observation 058edd71-0ee0-423a-bb3c-dc8f9fa004ad · outbound

This paper cites A Comprehensive Survey of Compression Algorithms for Language Models.

Zero-shot Quantization: A Comprehensive Survey A Comprehensive Survey of Compression Algorithms for Language Models

Reference 30

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Observation ae6a29fa-8691-4fc7-894d-e45b97c09292 · outbound

This paper cites Accurate sublayer pruning for large language models by exploiting latency and tunability information.

Zero-shot Quantization: A Comprehensive Survey Accurate sublayer pruning for large language models by exploiting latency and tunability information

Reference 31

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Observation 8d355f26-a9ac-4f7e-85e7-08480808235d · outbound

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Zero-shot Quantization: A Comprehensive Survey Unresolved cited work

Reference 32

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Observation c87e2722-0fb3-4098-abfd-e8110fd9efca · outbound

This paper cites Clamp-vit: contrastive data- free learning for adaptive post-training quantization of vits.

Zero-shot Quantization: A Comprehensive Survey Clamp-vit: contrastive data- free learning for adaptive post-training quantization of vits

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.312034Z

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Observation 8e1f5a1f-3294-4bc9-a2e7-80636b479a38 · outbound

This paper cites Causal- dfq: Causality guided data-free network quantization.

Zero-shot Quantization: A Comprehensive Survey Causal- dfq: Causality guided data-free network quantization

Reference 34

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Observation 83cfa1aa-13c6-4b67-966a-22ee49cfe0a1 · outbound

This paper cites A generalized zero- shot quantization of deep convolutional neural networks via learned weights statistics.

Zero-shot Quantization: A Comprehensive Survey A generalized zero- shot quantization of deep convolutional neural networks via learned weights statistics

Reference 35

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Observation 70a8a9c1-1038-40da-8c89-d9534344239f · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Zero-shot Quantization: A Comprehensive Survey Training data-efficient image transformers & distillation through attention

Reference 36

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Observation c0c1f7ba-6439-4d8f-8f44-2291a74a9cb6 · outbound

This paper cites Pruning has a disparate im- pact on model accuracy.

Zero-shot Quantization: A Comprehensive Survey Pruning has a disparate im- pact on model accuracy

Reference 37

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Observation 9aa03264-4c3c-43fe-a96c-822103616b19 · outbound

This paper cites Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization.

Zero-shot Quantization: A Comprehensive Survey Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization

Reference 38

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Observation dbb388f3-fa5e-4646-84de-a238e37c0048 · outbound

This paper cites Towards a smaller stu- dent: Capacity dynamic distillation for efficient image re- trieval.

Zero-shot Quantization: A Comprehensive Survey Towards a smaller stu- dent: Capacity dynamic distillation for efficient image re- trieval

Reference 39

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Observation fcaea6ba-b619-4fdf-b706-9eb5fab394b1 · outbound

This paper cites Generative low-bitwidth data free quantization.

Zero-shot Quantization: A Comprehensive Survey Generative low-bitwidth data free quantization

Reference 40

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Observation 33b0c522-80cc-47f5-969a-fa698fc2ce01 · outbound

This paper cites Dreaming to distill: Data-free knowl- edge transfer via deepinversion.

Zero-shot Quantization: A Comprehensive Survey Dreaming to distill: Data-free knowl- edge transfer via deepinversion

Reference 41

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Observation 8d8c433b-8d2e-4104-bf44-b3b8640f9df0 · outbound

This paper cites Knowledge extraction with no observable data.

Zero-shot Quantization: A Comprehensive Survey Knowledge extraction with no observable data

Reference 42

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Observation fd3a9871-fb35-4f93-b05e-44507dfc7a57 · outbound

This paper cites Diversifying sample generation for accurate data-free quantization.

Zero-shot Quantization: A Comprehensive Survey Diversifying sample generation for accurate data-free quantization

Reference 43

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Observation f3c1f300-aa84-4f18-89ce-ab9b0708aeb2 · outbound

This paper cites Intraq: Learning synthetic images with intra-class heterogeneity for zero-shot network quantiza- tion.

Zero-shot Quantization: A Comprehensive Survey Intraq: Learning synthetic images with intra-class heterogeneity for zero-shot network quantiza- tion

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.148291Z

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Observation 0408f7de-b3eb-46c5-bbbc-f0b35bbe853d · outbound

This paper cites Autorecon: Neural ar- chitecture search-based reconstruction for data-free com- pression.

Zero-shot Quantization: A Comprehensive Survey Autorecon: Neural ar- chitecture search-based reconstruction for data-free com- pression

Reference 45

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Observation fd20cfe2-eda0-4808-be4c-fa539b31bb2e · outbound

This paper cites Model compression and hardware ac- celeration for neural networks: A comprehensive survey.

Zero-shot Quantization: A Comprehensive Survey Model compression and hardware ac- celeration for neural networks: A comprehensive survey

Reference 2009

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Observation 27c595c9-9390-4c0e-b7a3-70d33093e1ac · outbound

This paper cites Squant: On-the-fly data-free quanti- zation via diagonal hessian approximation.

Zero-shot Quantization: A Comprehensive Survey Squant: On-the-fly data-free quanti- zation via diagonal hessian approximation

Reference 2014

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Observation d5290bda-906a-4e47-af6f-f80221d79f1f · outbound

This paper cites The knowledge within: Meth- ods for data-free model compression.

Zero-shot Quantization: A Comprehensive Survey The knowledge within: Meth- ods for data-free model compression

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.515000Z

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Observation c30ca05d-2dd7-42ff-bbaf-5ca15ff7c701 · outbound

This paper cites Sparse model inversion: Efficient inversion of vision transformers for data-free applications.

Zero-shot Quantization: A Comprehensive Survey Sparse model inversion: Efficient inversion of vision transformers for data-free applications

Reference 2016

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Observation 80876254-5679-48a9-ba84-d598b09e9bb3 · outbound

This paper cites Up or down? adaptive rounding for post-training quanti- zation.

Zero-shot Quantization: A Comprehensive Survey Up or down? adaptive rounding for post-training quanti- zation

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.354626Z

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Observation 81cf8514-b77c-40e0-b0b6-c1cdf83796b5 · outbound

This paper cites Texq: zero- shot network quantization with texture feature distribution calibration.

Zero-shot Quantization: A Comprehensive Survey Texq: zero- shot network quantization with texture feature distribution calibration

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.709316Z

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Observation ed893eb3-4561-4287-94f2-96f19dd0824e · outbound

This paper cites It’s all in the teacher: Zero-shot quantization brought closer to the teacher.

Zero-shot Quantization: A Comprehensive Survey It’s all in the teacher: Zero-shot quantization brought closer to the teacher

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.665982Z

Source-reported events for the cited work

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Observation a0f6a4c9-c01a-4021-bd82-7540eba761ca · outbound

This paper cites Qimera: Data-free quantization with synthetic boundary supporting samples.

Zero-shot Quantization: A Comprehensive Survey Qimera: Data-free quantization with synthetic boundary supporting samples

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.680304Z

Source-reported events for the cited work

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Observation 42dc4d0f-fe7d-4f40-993e-5b1a103bea4e · outbound

This paper cites Robustness-guided image synthesis for data-free quanti- zation.

Zero-shot Quantization: A Comprehensive Survey Robustness-guided image synthesis for data-free quanti- zation

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.738185Z

Source-reported events for the cited work

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Observation b899192d-88ec-4027-b5fb-a310737c7371 · outbound

This paper cites Ze- roq: A novel zero shot quantization framework.

Zero-shot Quantization: A Comprehensive Survey Ze- roq: A novel zero shot quantization framework

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.723628Z

Source-reported events for the cited work

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Observation 919692a6-96fa-4a93-8b5c-f9a7c4f95fed · outbound

This paper cites Gra- dient harmonized single-stage detector.

Zero-shot Quantization: A Comprehensive Survey Gra- dient harmonized single-stage detector

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:40:04.439086Z

Source-reported events for the cited work

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Pith citing papers

Observation fc943326-4ff6-44e5-83ad-86896860e62b · inbound

Compute Only Once: UG-Separation for Efficient Large Recommendation Models cites this paper.

Compute Only Once: UG-Separation for Efficient Large Recommendation Models Zero-shot Quantization: A Comprehensive Survey

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.