Paper Citation Record · LEDGER

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification

As of 22 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.01368.

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pith.paper-citation-record.v1
2506.01368 v1

Coverage vector

measured 36 of 36 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T11:50:22.291753Z

measured 36 of 36 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.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 893f7937-e169-41e0-84ac-52244bba147a · outbound

This paper cites Long-tailed recognition via weight balancing.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Long-tailed recognition via weight balancing

Reference 1

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Observation fa77c68f-1f52-424e-885f-8c1e6dab6529 · outbound

This paper cites A Note on the Inception Score.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification A Note on the Inception Score

Reference 2

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Observation df1fb1ee-1ef7-4d34-9a3c-ea73027a712d · outbound

This paper cites Food-101–mining discriminative components with random forests.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Food-101–mining discriminative components with random forests

Reference 3

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Observation 02abb9de-fe6e-43f6-8ab5-fb4833caa50c · outbound

This paper cites A systematic study of the class imbalance problem in convolu- tional neural networks.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification A systematic study of the class imbalance problem in convolu- tional neural networks

Reference 4

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Observation 1142e4cf-01c3-4e6a-888a-a30cbea14014 · outbound

This paper cites Learning imbalanced datasets with label- distribution-aware margin loss.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Learning imbalanced datasets with label- distribution-aware margin loss

Reference 5

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Observation 9a66ab8a-34d9-42e9-8700-05648de4aec4 · outbound

This paper cites ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts

Reference 6

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Observation b5a0a718-d25d-4811-9b6a-fc7820df508a · outbound

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

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Improved Regularization of Convolutional Neural Networks with Cutout

Reference 7

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Observation 3fca94c5-9c89-44c2-a02f-2e8e863d7d8a · outbound

This paper cites Diffusion models beat gans on image synthesis.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Diffusion models beat gans on image synthesis

Reference 8

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Observation aac87ed3-7dfc-48d5-aac2-bf9c9cbfcc83 · outbound

This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized sta- tistical models.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Noise-contrastive estimation: A new estimation principle for unnormalized sta- tistical models

Reference 9

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Observation 4ef1f689-1d6b-4a71-a28c-42058bcb0e94 · outbound

This paper cites Latent-based diffusion model for long-tailed recognition.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Latent-based diffusion model for long-tailed recognition

Reference 10

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Observation bca396a0-a105-4a13-984a-e87bbbd464d1 · outbound

This paper cites Diffusion model with clustering-based con- ditioning for food image generation.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Diffusion model with clustering-based con- ditioning for food image generation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:24.784832Z

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Observation aa9c6a01-5209-40d4-a905-1b4ce56173b3 · outbound

This paper cites Single-stage heavy-tailed food classification.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Single-stage heavy-tailed food classification

Reference 12

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Observation 0f6e0875-8f5f-4d56-9945-1673636e8335 · outbound

This paper cites Multi-task image-based dietary assessment for food recognition and portion size es- timation.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Multi-task image-based dietary assessment for food recognition and portion size es- timation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:24.520663Z

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Observation 877a7078-e941-4097-a5bb-e1e59dc94ee7 · outbound

This paper cites Long-tailed food classification.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Long-tailed food classification

Reference 14

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Observation 9dc768ed-470e-4aba-933e-9dbe837915ce · outbound

This paper cites Deep residual learning for image recognition.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Deep residual learning for image recognition

Reference 15

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Observation 4a2e00e1-943e-4f30-9fa7-a83ee1770565 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Classifier-Free Diffusion Guidance

Reference 16

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Observation 469a3e25-e2b9-41a5-bdf4-4392304e9643 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Denoising dif- fusion probabilistic models

Reference 17

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Observation 431084f3-5f5b-4e1b-a183-b1eb55c15053 · outbound

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

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Diffusemix: Label- preserving data augmentation with diffusion models

Reference 18

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Observation a556d40e-467a-4fd5-b77b-df00c81d576e · outbound

This paper cites Dynamic Negative Guidance of Diffusion Models.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Dynamic Negative Guidance of Diffusion Models

Reference 19

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Observation f5f61380-5ee0-401b-aad4-2c2ae388a6b5 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 20

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Observation a8956490-a492-4810-8418-4055da685971 · outbound

This paper cites Visual aware hierarchy based food recognition.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Visual aware hierarchy based food recognition

Reference 21

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Observation 320774af-3b8c-44e3-b351-eb4d59e03e70 · outbound

This paper cites Long-tail learning via logit adjustment.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Long-tail learning via logit adjustment

Reference 22

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Observation ff376009-61d9-434f-815f-8be131fa5b2b · outbound

This paper cites Co- synthesis of histopathology nuclei image-label pairs using a context-conditioned joint diffusion model.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Co- synthesis of histopathology nuclei image-label pairs using a context-conditioned joint diffusion model

Reference 23

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Observation fc7278f8-810c-4b26-a975-62711c254826 · outbound

This paper cites Diffmix: Diffusion model- based data synthesis for nuclei segmentation and classifica- tion in imbalanced pathology image datasets.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Diffmix: Diffusion model- based data synthesis for nuclei segmentation and classifica- tion in imbalanced pathology image datasets

Reference 24

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Observation e4ed1b62-76d6-43be-9f51-17613ff0c3e2 · outbound

This paper cites Controllable and efficient multi-class pathology nuclei data augmentation using text- conditioned diffusion models.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Controllable and efficient multi-class pathology nuclei data augmentation using text- conditioned diffusion models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:23.498989Z

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Observation 86aace5c-2cee-4617-8497-8cf2e4ebfc36 · outbound

This paper cites Training lan- guage models to follow instructions with human feedback.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Training lan- guage models to follow instructions with human feedback

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:23.408789Z

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Observation 243c6be1-3ec8-4650-b8da-8893775c377b · outbound

This paper cites Influence-balanced loss for imbalanced visual clas- sification.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Influence-balanced loss for imbalanced visual clas- sification

Reference 27

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Observation 823a7f78-20d8-4da9-bcc0-de77604104b9 · outbound

This paper cites The majority can help the minority: Context-rich minority oversampling for long-tailed classifi- cation.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification The majority can help the minority: Context-rich minority oversampling for long-tailed classifi- cation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:23.245836Z

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Observation 63636fe3-58d3-4723-a962-a5eb389951bc · outbound

This paper cites Balanced meta-softmax for long-tailed visual recog- nition.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Balanced meta-softmax for long-tailed visual recog- nition

Reference 29

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Observation 1d9cd14d-253a-49c0-9b26-2ce51121db7c · outbound

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

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification High-resolution image syn- thesis with latent diffusion models

Reference 30

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Observation 0f9dfe02-e084-459b-ab10-7f8cf7a206d4 · outbound

This paper cites Focal loss for dense object detection.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Focal loss for dense object detection

Reference 31

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Source-reported events for the cited work

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Observation 00b659a9-09ab-402f-b692-30e75739654e · outbound

This paper cites CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling

Reference 32

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Observation 5568c75f-328f-4703-b599-9eef61ef4b16 · outbound

This paper cites Experimental perspectives on learning from imbal- anced data.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Experimental perspectives on learning from imbal- anced data

Reference 33

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Observation eedc11b9-3db2-4196-a176-da2257fd3311 · outbound

This paper cites SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems

Reference 34

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Observation f68e6dbe-5807-49dc-96b5-bc2926c324b9 · outbound

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

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:22.658236Z

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Observation a1ba2866-30e4-4315-bcd8-16f4b3d02792 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification mixup: Beyond Empirical Risk Minimization

Reference 36

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