Paper Citation Record · LEDGER

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting

As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.21947.

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

pith.paper-citation-record.v1
2507.21947 v1

Coverage vector

measured 39 of 39 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T12:18:20.161762Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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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

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Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 32accd79-edaf-4c49-8219-4a613005710c · outbound

This paper cites Can we use gradient norm as a measure of generalization error for model selection in prac- tice? 2021.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Can we use gradient norm as a measure of generalization error for model selection in prac- tice? 2021

Reference 1

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Observation 06e7068d-f406-4f47-a8d4-b0d12a181049 · outbound

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

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Qimera: Data-free quantization with synthetic boundary supporting samples

Reference 2

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Observation 6f09decc-4b35-4fad-8846-c63945c1606a · outbound

This paper cites MimiQ: Low-Bit Data-Free Quantization of Vision Transformers with Encouraging Inter-Head Attention Similarity.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting MimiQ: Low-Bit Data-Free Quantization of Vision Transformers with Encouraging Inter-Head Attention Similarity

Reference 3

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Observation 136fa22e-d4d7-4b91-8413-b72c5b3dbf3c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

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

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Sharpness-aware data generation for zero- shot quantization

Reference 5

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Observation f3e829a7-dc57-40b1-ad39-952da889a80b · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 6

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Observation 298f585d-0a4b-47a7-a2b2-58a9d92b6b36 · outbound

This paper cites Scaling laws of synthetic images for model training.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Scaling laws of synthetic images for model training

Reference 7

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This paper cites Deep residual learning for image recognition.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Deep residual learning for image recognition

Reference 8

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Observation bc4a2ee5-c593-4188-a4b6-cdca5ded8ab0 · outbound

This paper cites Is synthetic data from generative models ready for image recognition?.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Is synthetic data from generative models ready for image recognition?

Reference 9

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Observation 0b38d189-9426-4299-96b1-fbc171065a87 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 10

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Observation ba073a13-ef52-4872-b9e1-30aef92ed8d8 · outbound

This paper cites Difficulty diversity and plausibility: Dynamic data-free quantization.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Difficulty diversity and plausibility: Dynamic data-free quantization

Reference 11

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Observation 81164b36-166b-4b36-b3a4-b671b331b11f · outbound

This paper cites Diffusemix: Label- preserving data augmentation with diffusion models.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Diffusemix: Label- preserving data augmentation with diffusion models

Reference 12

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Observation b0bf9b85-0cb6-4211-b01d-51a424e4c121 · outbound

This paper cites Ge- nie: show me the data for quantization.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Ge- nie: show me the data for quantization

Reference 13

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Observation e0d2f7bf-be65-47d1-b85d-5b765f291c8d · outbound

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

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Learning multiple layers of features from tiny images

Reference 14

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Observation 6755b5c1-cb7f-45d3-acec-0c5b35018e99 · outbound

This paper cites Flexround: Learnable rounding based on element- wise division for post-training quantization.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Flexround: Learnable rounding based on element- wise division for post-training quantization

Reference 15

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Observation 3b9f707a-f1af-43ba-a2a6-44621481f856 · outbound

This paper cites On generaliza- tion error bounds of noisy gradient methods for non-convex learning.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting On generaliza- tion error bounds of noisy gradient methods for non-convex learning

Reference 16

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Observation 5e1ddbc5-3166-403c-9e62-7f788d776a23 · outbound

This paper cites Brecq: Pushing the limit of post-training quantization by block reconstruc- tion.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Brecq: Pushing the limit of post-training quantization by block reconstruc- tion

Reference 17

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Observation cfd49d4b-6770-4a19-9a57-618ea626468c · outbound

This paper cites Mixmix: All you need for data-free compression are feature and data mix- ing.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Mixmix: All you need for data-free compression are feature and data mix- ing

Reference 18

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This paper cites Genq: Quantization in low data regimes with generative synthetic data.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Genq: Quantization in low data regimes with generative synthetic data

Reference 19

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Observation cde51ea0-6a55-44e3-8cd8-e634e085ac3e · outbound

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

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Patch similarity aware data-free quantization for vision transformers

Reference 20

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This paper cites Repq- vit: Scale reparameterization for post-training quantization of vision transformers.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Repq- vit: Scale reparameterization for post-training quantization of vision transformers

Reference 21

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This paper cites Pd-quant: Post-training quantiza- tion based on prediction difference metric.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Pd-quant: Post-training quantiza- tion based on prediction difference metric

Reference 22

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Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Swin transformer: Hierarchical vision transformer using shifted windows

Reference 23

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Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Unresolved cited work

Reference 24

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This paper cites Metaaug: Meta-data augmentation for post-training quanti- zation.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Metaaug: Meta-data augmentation for post-training quanti- zation

Reference 25

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This paper cites Adaptive data-free quantization.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Adaptive data-free quantization

Reference 26

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Observation 7f68c1ab-e6ef-4e3b-ba01-98c4aeec3477 · outbound

This paper cites ResizeMix: Mixing Data with Preserved Object Information and True Labels.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting ResizeMix: Mixing Data with Preserved Object Information and True Labels

Reference 27

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This paper cites High-resolution image syn- thesis with latent diffusion models.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting High-resolution image syn- thesis with latent diffusion models

Reference 28

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Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Imagenet large scale visual recognition challenge

Reference 29

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Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 30

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This paper cites Fake it till you make it: Learning trans- ferable representations from synthetic imagenet clones.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Fake it till you make it: Learning trans- ferable representations from synthetic imagenet clones

Reference 31

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Observation 6d634219-e0b8-4262-9302-2f80d052f6e8 · outbound

This paper cites Learning vision from models rivals learning vision from data.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Learning vision from models rivals learning vision from data

Reference 32

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

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Training data-efficient image transformers & distillation through at- tention

Reference 33

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This paper cites Enhance im- age classification via inter-class image mixup with diffusion model.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Enhance im- age classification via inter-class image mixup with diffusion model

Reference 34

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This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 35

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This paper cites Schr\"{o}dinger's Bat: Diffusion Models Sometimes Generate Polysemous Words in Superposition.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Schr\"{o}dinger's Bat: Diffusion Models Sometimes Generate Polysemous Words in Superposition

Reference 36

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This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 37

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This paper cites Dauphin, and David Lopez-Paz.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting Dauphin, and David Lopez-Paz

Reference 38

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Observation 7c5310fe-66d1-4eb0-a6c2-2d10c4c4099d · outbound

This paper cites 1, 2, 3, 4, 6.

Enhancing Generalization in Data-free Quantization via Mixup-class Prompting 1, 2, 3, 4, 6

Reference 235

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:23.222475Z

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Pith citing papers

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