Paper Citation Record · LEDGER

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks

As of 10 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2509.05307.

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

pith.paper-citation-record.v1
2509.05307 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:14:24.657287Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 3ef1a5e0-da46-422c-857b-36d36cee510f · outbound

This paper cites Patchswap: A regularization technique for vision transformers.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Patchswap: A regularization technique for vision transformers

Reference 1

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Observation d29c826d-b58a-4432-8c5d-47370baa9e71 · outbound

This paper cites Generative alignment of pos- terior probabilities for source-free domain adaptation.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Generative alignment of pos- terior probabilities for source-free domain adaptation

Reference 2

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Observation f8f595fc-18f2-4ed6-8c16-193e2705b639 · outbound

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

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Imagenet: A large-scale hierarchical image database

Reference 3

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Observation af9fc86f-c320-462c-985e-460339796324 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

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Observation 7d3ed51f-cc70-43df-b046-5f25f3e723b6 · outbound

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

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Improved Regularization of Convolutional Neural Networks with Cutout

Reference 5

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Observation d29781eb-5eea-4b17-8faa-8465f1e8d0b8 · outbound

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

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks An image is worth 16x16 words: Transformers for image recognition at scale

Reference 6

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Observation c130d601-d43a-4d97-a567-02b99ec3b05b · outbound

This paper cites Keepaugment: A simple information-preserving data augmentation approach.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Keepaugment: A simple information-preserving data augmentation approach

Reference 7

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Observation 05b75ff4-9768-4432-b21f-ba03885f1169 · outbound

This paper cites Deep residual learning for image recognition.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Deep residual learning for image recognition

Reference 8

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Observation 8c15742f-67d1-47b8-b54c-9a6fd50fccfa · outbound

This paper cites Augmix: A simple data processing method to improve ro- bustness and uncertainty.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Augmix: A simple data processing method to improve ro- bustness and uncertainty

Reference 9

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Observation 3cf0027b-6dad-4a22-84d0-9f48e0e0c2c6 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Distilling the Knowledge in a Neural Network

Reference 10

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Observation 67ed2edd-76a7-4e72-8e4a-a3030b5593da · outbound

This paper cites Densely connected convolutional networks.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Densely connected convolutional networks

Reference 11

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Observation de92beb1-a2b2-485a-9266-1f244343294e · outbound

This paper cites Large-scale video classification with convolutional neural networks.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Large-scale video classification with convolutional neural networks

Reference 12

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Observation eff936a6-c6aa-4262-ad6c-5f32d1211720 · outbound

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

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Learning multiple layers of features from tiny images

Reference 13

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Observation 24d655df-4c27-4b8b-a0af-bfe77cd36348 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Imagenet classification with deep convolutional neural networks

Reference 14

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Observation d13ebb49-0ad2-48ee-8307-d3c43b28802b · outbound

This paper cites Hmdb: a large video database for human motion recognition.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Hmdb: a large video database for human motion recognition

Reference 15

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Observation 8e214732-9705-4246-85c7-f066b427fe85 · outbound

This paper cites Gradient-based learn- ing applied to document recognition.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Gradient-based learn- ing applied to document recognition

Reference 16

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Observation 554ed806-e14d-4ef2-879a-6762ad0da9c3 · outbound

This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:14:25.178172Z

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Observation 1af19263-0dde-4c84-95c2-1e8e505a479b · outbound

This paper cites Focal loss for dense object detection.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Focal loss for dense object detection

Reference 18

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Observation b0da0372-ab55-4f92-a309-d216502fba37 · outbound

This paper cites The devil is in the margin: Margin-based label smoothing for network calibration.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks The devil is in the margin: Margin-based label smoothing for network calibration

Reference 19

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Observation 9728b592-9295-4ae7-a44d-9ca4245f8f5f · outbound

This paper cites Calibrating deep neural networks using focal loss.Advances in Neural Information Processing Systems, 33:15288–15299, 2020.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Calibrating deep neural networks using focal loss.Advances in Neural Information Processing Systems, 33:15288–15299, 2020

Reference 20

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Observation ee4c1c03-921b-431f-99a1-f9b9e9ba05c9 · outbound

This paper cites When does label smoothing help? Advances in neural information processing systems , 32, 2019.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks When does label smoothing help? Advances in neural information processing systems , 32, 2019

Reference 21

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Observation ddfe7bc7-3653-4c86-8606-a68691a08859 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Reading digits in natural images with unsupervised feature learning

Reference 22

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Observation 5d758765-0317-4234-962e-c2f3b1f49df1 · outbound

This paper cites Rethinking CNN Models for Audio Classification.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Rethinking CNN Models for Audio Classification

Reference 23

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Observation 1d56fcae-7aff-4a47-bf23-01bd3e4baf87 · outbound

This paper cites an unresolved cited work.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Unresolved cited work

Reference 24

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Observation d8bd59c9-a4f4-4f24-aa5f-1b77edc1dff6 · outbound

This paper cites SELFIE: Refurbishing unclean sam- ples for robust deep learning.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks SELFIE: Refurbishing unclean sam- ples for robust deep learning

Reference 25

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Observation 15cf4dd5-70e1-42b5-9514-e4443c1987fd · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 26

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Observation 9b144952-b1e6-49cf-811f-0fda0f0d55da · outbound

This paper cites Rethinking the inception architecture for computer vision.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Rethinking the inception architecture for computer vision

Reference 27

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Observation c455a007-772b-4d40-9027-d401fa15a1f8 · outbound

This paper cites Learning spatiotemporal features with 3d convolutional networks.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Learning spatiotemporal features with 3d convolutional networks

Reference 28

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Observation f7d2ca07-6159-4678-bc35-b74684fda34f · outbound

This paper cites Automatic musical genre classification of audio signals.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Automatic musical genre classification of audio signals

Reference 29

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Observation 2de467d2-22d0-4ce3-9c5e-6d79272db600 · outbound

This paper cites Visualizing data using t-sne.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Visualizing data using t-sne

Reference 30

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Observation 2fd2de89-1083-46c8-9859-506acc3af0cd · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 31

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This paper cites Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017

Reference 32

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Observation 4d9dced0-330f-4f65-8498-3b08b9cd9d60 · outbound

This paper cites Revisiting knowl- edge distillation via label smoothing regularization.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Revisiting knowl- edge distillation via label smoothing regularization

Reference 33

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Observation 32afbd33-e185-4539-868a-249f8e8f2e9f · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with local- izable features.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Cutmix: Regularization strategy to train strong classifiers with local- izable features

Reference 34

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Observation 4c58e38b-6428-43a7-b338-0e1e9bf13480 · outbound

This paper cites Delving deep into label smoothing.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Delving deep into label smoothing

Reference 35

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This paper cites Dauphin, and David Lopez-Paz.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Dauphin, and David Lopez-Paz

Reference 36

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Observation a1198006-8837-40c4-8252-c9c0fbaa35a5 · outbound

This paper cites Character-level convolutional networks for text classification.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Character-level convolutional networks for text classification

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:14:24.918911Z

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Observation 419ac6af-8c0a-4e94-91ad-74fb90c7c3f0 · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural network for mobile devices.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks Shufflenet: An extremely efficient convolutional neural network for mobile devices

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:14:24.902253Z

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Observation 5b6704d2-6471-47e3-b79c-9be3f0849f92 · outbound

This paper cites doi: 10.1109/ICCV .2019.00612.

Label Smoothing++: Enhanced Label Regularization for Training Neural Networks doi: 10.1109/ICCV .2019.00612

Reference 6031

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Pith citing papers

No inbound Pith citation observations are available.