Paper Citation Record · LEDGER

Data Augmentation For Small Object using Fast AutoAugment

As of 7 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.08956.

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

pith.paper-citation-record.v1
2506.08956 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:00:07.802440Z

measured 20 of 20 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 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

20 of 20 outbound references displayed

verified exact0
verified fuzzy11
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 18d156fc-b9bd-47bc-a072-b7159c2e14d2 · outbound

This paper cites Microsoft coco: Common objects in context.

Data Augmentation For Small Object using Fast AutoAugment Microsoft coco: Common objects in context

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:05.093161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9041b838-c2c5-4520-ad9f-204f8d2fc6d1 · outbound

This paper cites Random erasing data augmentation.

Data Augmentation For Small Object using Fast AutoAugment Random erasing data augmentation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:05.196234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b093023-8452-4df4-a9bc-c64acb266e9b · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Data Augmentation For Small Object using Fast AutoAugment Improved Regularization of Convolutional Neural Networks with Cutout

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:05.315834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1b0ccd0-678d-4223-82ec-3234dee665a8 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Data Augmentation For Small Object using Fast AutoAugment mixup: Beyond Empirical Risk Minimization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:05.416079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d90a1ebb-4a29-4d37-a801-c064155f701d · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Data Augmentation For Small Object using Fast AutoAugment Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:05.544334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6b629c5-2ecd-4180-bd88-fd5ea74029cc · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.Advances in neural in- formation processing systems, 28, 2015.

Data Augmentation For Small Object using Fast AutoAugment Faster r-cnn: Towards real-time object detection with region proposal networks.Advances in neural in- formation processing systems, 28, 2015

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:09.261957Z

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 1810b5bd-e837-4ecf-8f7b-921bcda32a14 · outbound

This paper cites Object detection in aerial images: A large-scale benchmark and challenges.IEEE Trans- actions on Pattern Analysis and Machine Intelligence, pages 1–1, 2021.

Data Augmentation For Small Object using Fast AutoAugment Object detection in aerial images: A large-scale benchmark and challenges.IEEE Trans- actions on Pattern Analysis and Machine Intelligence, pages 1–1, 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:09.136562Z

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 b99c03c6-8300-436b-acb2-96cdd45a761b · outbound

This paper cites Rich feature hierarchiesforaccurateobjectdetectionandsemanticsegmentation.

Data Augmentation For Small Object using Fast AutoAugment Rich feature hierarchiesforaccurateobjectdetectionandsemanticsegmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:09.006130Z

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 b8318591-d55a-4525-8732-862ce6d296d1 · outbound

This paper cites You only look once: Unified, real-time object detection.

Data Augmentation For Small Object using Fast AutoAugment You only look once: Unified, real-time object detection

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:06.186911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70310994-4506-4414-ad1a-763db8a65538 · outbound

This paper cites Ssd: Single shot multibox detector.

Data Augmentation For Small Object using Fast AutoAugment Ssd: Single shot multibox detector

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:08.886600Z

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 4bb60502-ae66-47ed-9951-5315249f5bd8 · outbound

This paper cites Scale-transferrable object detection.

Data Augmentation For Small Object using Fast AutoAugment Scale-transferrable object detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:08.841756Z

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 67e5bac6-c131-4d12-bbed-e0831f95b4a9 · outbound

This paper cites Stdnet: A convnet for small target detection.

Data Augmentation For Small Object using Fast AutoAugment Stdnet: A convnet for small target detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:08.674449Z

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 f12132bd-9ae6-4903-a372-595c2269e0e1 · outbound

This paper cites Augmentation for small object detection.

Data Augmentation For Small Object using Fast AutoAugment Augmentation for small object detection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:06.741430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2091d6f3-8b96-46b4-ad26-96b662f3d12d · outbound

This paper cites Autoaugment: Learning augmentation strategies from data.

Data Augmentation For Small Object using Fast AutoAugment Autoaugment: Learning augmentation strategies from data

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:08.476285Z

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 91648c80-8a81-4376-a3bb-bfd53ce9453c · outbound

This paper cites Population based augmentation: Efficient learning of augmentation policy schedules.

Data Augmentation For Small Object using Fast AutoAugment Population based augmentation: Efficient learning of augmentation policy schedules

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:08.355505Z

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 950a30b9-166e-4b51-881f-b0706e9f49ce · outbound

This paper cites Fast autoaugment.

Data Augmentation For Small Object using Fast AutoAugment Fast autoaugment

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:08.192053Z

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 b4558740-0e8f-4cf8-94a7-c500ed85c629 · outbound

This paper cites Algorithms for hyper-parameter optimization.Advances in neural information processing systems, 24, 2011.

Data Augmentation For Small Object using Fast AutoAugment Algorithms for hyper-parameter optimization.Advances in neural information processing systems, 24, 2011

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:08.078052Z

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 26f9fdd5-f5a7-4078-bd68-bc68ae9733a3 · outbound

This paper cites Pedregosa, G.

Data Augmentation For Small Object using Fast AutoAugment Pedregosa, G

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:07.481861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d552bbf-f62a-4331-a57b-ee4bd06f1d42 · outbound

This paper cites Ray: A distributed framework for emerging {AI} applications.

Data Augmentation For Small Object using Fast AutoAugment Ray: A distributed framework for emerging {AI} applications

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:07.994020Z

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 59ba1d3e-4d59-4a77-870d-051af5addb0a · outbound

This paper cites Focal loss for dense object detection.

Data Augmentation For Small Object using Fast AutoAugment Focal loss for dense object detection

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:07.802440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.