Paper Citation Record · LEDGER

CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1905.04899.

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

pith.paper-citation-record.v1
1905.04899 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:51:49.849008Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T17:23:02.696387Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f426121c-df0f-4739-931c-1d5956649059 · inbound

DP-DocLDM: Differentially Private Document Image Generation using Latent Diffusion Models cites this paper.

DP-DocLDM: Differentially Private Document Image Generation using Latent Diffusion Models CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T00:51:49.849008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a0494e5-daa6-470f-8828-76e349d8abaf · inbound

Enhancing compact convolutional transformers with super attention cites this paper.

Enhancing compact convolutional transformers with super attention CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-05T16:06:14.452828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60519be0-1fb8-4fc2-9874-8d1d0aad0a32 · inbound

R\'enyi Attention Entropy for Patch Pruning cites this paper.

R\'enyi Attention Entropy for Patch Pruning CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:23:02.698775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7ea8e807-1f0d-4014-b806-53a8882f18cd · inbound

D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks cites this paper.

D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:46:04.782548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d9c9244c-3609-4a6b-b48b-bff7c0c4b216 · inbound

A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence cites this paper.

A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.897544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c324d12-313a-411d-b0d8-36dc4a35f75e · inbound

Medical Model Synthesis Architectures: A Case Study cites this paper.

Medical Model Synthesis Architectures: A Case Study CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Reference 176

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:25.023524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2dba05d-e57c-4a3b-96ec-0e6838091300 · inbound

Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees cites this paper.

Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-01T02:43:34.475406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.