Paper Citation Record · LEDGER

Dataset Augmentation by Mixing Visual Concepts

As of 12 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2412.15358.

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

pith.paper-citation-record.v1
2412.15358 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:32:39.176197Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

48 of 48 outbound references displayed

verified exact0
verified fuzzy24
unresolved24
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dab5016e-85ab-464c-9350-d5616f20c688 · outbound

This paper cites Synthetic Data from Diffusion Models Improves ImageNet Classification.

Dataset Augmentation by Mixing Visual Concepts Synthetic Data from Diffusion Models Improves ImageNet Classification

Reference 1

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Observation 0738c03b-998e-4032-b5ef-f7354a591535 · outbound

This paper cites This dataset does not exist: training models from generated images.

Dataset Augmentation by Mixing Visual Concepts This dataset does not exist: training models from generated images

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:40.241467Z

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Observation 8fc04f4c-da42-48a9-a275-eb88d6ccd01b · outbound

This paper cites Deep generative modelling: A comparative re- view of vaes, gans, normalizing flows, energy-based and au- toregressive models.

Dataset Augmentation by Mixing Visual Concepts Deep generative modelling: A comparative re- view of vaes, gans, normalizing flows, energy-based and au- toregressive models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:40.216294Z

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Observation 7d18d5c1-8d7d-46e1-80fb-8763cb85538b · outbound

This paper cites In- structpix2pix: Learning to follow image editing instructions.

Dataset Augmentation by Mixing Visual Concepts In- structpix2pix: Learning to follow image editing instructions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:40.198140Z

Source-reported events for the cited work

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Observation 022177e7-a154-45ee-84aa-1ef441f27cca · outbound

This paper cites Brain tumor mri dataset, 2023.

Dataset Augmentation by Mixing Visual Concepts Brain tumor mri dataset, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:40.175644Z

Source-reported events for the cited work

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Observation 8f227cbc-aa3b-4284-9adc-3648a794544a · outbound

This paper cites A review of medical image data augmentation techniques for deep learning appli- cations.

Dataset Augmentation by Mixing Visual Concepts A review of medical image data augmentation techniques for deep learning appli- cations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:40.156512Z

Source-reported events for the cited work

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Observation aaeb4556-87dd-48e5-aeb9-5061645ef83e · outbound

This paper cites Diffusion models in vision: A survey.

Dataset Augmentation by Mixing Visual Concepts Diffusion models in vision: A survey

Reference 7

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Source-reported events for the cited work

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Observation 03b4619c-706a-486c-8898-187830541461 · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Dataset Augmentation by Mixing Visual Concepts AutoAugment: Learning Augmentation Policies from Data

Reference 8

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Source-reported events for the cited work

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Observation 8a8dacb3-f85e-4891-8efd-ac059662f817 · outbound

This paper cites Autoaugment: Learning augmentation strategies from data.

Dataset Augmentation by Mixing Visual Concepts Autoaugment: Learning augmentation strategies from data

Reference 9

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Observation 4916c4b8-cac7-4a7f-be0d-675a6cbecbaf · outbound

This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

Dataset Augmentation by Mixing Visual Concepts Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:40.086678Z

Source-reported events for the cited work

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Observation 528f00b6-dfd3-4ae2-a59b-86398d95d778 · outbound

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

Dataset Augmentation by Mixing Visual Concepts Imagenet: A large-scale hierarchical image database

Reference 11

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Source-reported events for the cited work

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Observation 3dba07e6-c20d-414c-bafd-e89723313a80 · outbound

This paper cites Dataset Augmentation in Feature Space.

Dataset Augmentation by Mixing Visual Concepts Dataset Augmentation in Feature Space

Reference 12

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Source-reported events for the cited work

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Observation f40d0391-225d-4999-8035-9929624c3ac8 · outbound

This paper cites Jukebox: A Generative Model for Music.

Dataset Augmentation by Mixing Visual Concepts Jukebox: A Generative Model for Music

Reference 13

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Source-reported events for the cited work

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Observation 0f957680-b8d2-45c4-84ca-ddd4bbfa82e8 · outbound

This paper cites Flownet: Learning optical flow with convolutional networks.

Dataset Augmentation by Mixing Visual Concepts Flownet: Learning optical flow with convolutional networks

Reference 14

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Source-reported events for the cited work

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Observation 369c1ec3-0e7e-48d1-848f-f09b63b87eb4 · outbound

This paper cites One-shot learning of object categories.

Dataset Augmentation by Mixing Visual Concepts One-shot learning of object categories

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:40.019729Z

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Observation b0f00283-3b8f-471b-8ace-00d7dce5ec36 · outbound

This paper cites Generative adversarial nets.

Dataset Augmentation by Mixing Visual Concepts Generative adversarial nets

Reference 16

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Source-reported events for the cited work

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Observation e9083bbb-09a7-483b-a292-92ad51c99964 · outbound

This paper cites A review on generative adversarial networks: Algorithms, theory, and applications.

Dataset Augmentation by Mixing Visual Concepts A review on generative adversarial networks: Algorithms, theory, and applications

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.979996Z

Source-reported events for the cited work

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Observation fb51f3d9-a5ec-4569-b5a1-12f098664c81 · outbound

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

Dataset Augmentation by Mixing Visual Concepts Is synthetic data from generative models ready for image recognition?

Reference 18

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Source-reported events for the cited work

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Observation 7fdeee32-a3f1-42e4-82ad-62944682089b · outbound

This paper cites Denoising dif- fusion probabilistic models.

Dataset Augmentation by Mixing Visual Concepts Denoising dif- fusion probabilistic models

Reference 19

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Source-reported events for the cited work

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Observation b56c291a-fca1-4887-8c6c-ff367a7efe3f · outbound

This paper cites Classifier-Free Diffusion Guidance.

Dataset Augmentation by Mixing Visual Concepts Classifier-Free Diffusion Guidance

Reference 20

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Source-reported events for the cited work

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Observation c0e5d8db-e560-4296-b353-d3aba37cb355 · outbound

This paper cites Generative Models as a Data Source for Multiview Representation Learning.

Dataset Augmentation by Mixing Visual Concepts Generative Models as a Data Source for Multiview Representation Learning

Reference 21

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Source-reported events for the cited work

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Observation b3c1ab27-6b5b-416c-b1c9-2c676ac64963 · outbound

This paper cites Distilling Model Failures as Directions in Latent Space.

Dataset Augmentation by Mixing Visual Concepts Distilling Model Failures as Directions in Latent Space

Reference 22

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Source-reported events for the cited work

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Observation 6c54d049-1345-4cea-86b2-9a4101231d69 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Dataset Augmentation by Mixing Visual Concepts A style-based generator architecture for generative adversarial networks

Reference 23

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Source-reported events for the cited work

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Observation 8f1f53ca-5079-4812-94bd-e36691a17f36 · outbound

This paper cites Deep Directed Generative Models with Energy-Based Probability Estimation.

Dataset Augmentation by Mixing Visual Concepts Deep Directed Generative Models with Energy-Based Probability Estimation

Reference 24

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Source-reported events for the cited work

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Observation eddd6dc8-7957-4fc1-b433-23baa6ed31ab · outbound

This paper cites Auto-Encoding Variational Bayes.

Dataset Augmentation by Mixing Visual Concepts Auto-Encoding Variational Bayes

Reference 25

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Source-reported events for the cited work

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Observation cc82f34b-4b58-4f85-9f08-19fd8e2211c5 · outbound

This paper cites An introduction to variational autoencoders.

Dataset Augmentation by Mixing Visual Concepts An introduction to variational autoencoders

Reference 26

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Source-reported events for the cited work

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Observation 8e5e13b3-c0f2-4d3f-a122-17eb3192feab · outbound

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

Dataset Augmentation by Mixing Visual Concepts Learning multiple layers of features from tiny images

Reference 27

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Source-reported events for the cited work

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Observation 3a2e4f60-2e69-4b3f-9f56-12e87bf04985 · outbound

This paper cites Tiny imagenet visual recognition challenge.

Dataset Augmentation by Mixing Visual Concepts Tiny imagenet visual recognition challenge

Reference 28

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Source-reported events for the cited work

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Observation b27fc310-d7c4-46a4-b1a3-f257acbe4614 · outbound

This paper cites Smart augmentation learning an optimal data augmentation strategy.

Dataset Augmentation by Mixing Visual Concepts Smart augmentation learning an optimal data augmentation strategy

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.863390Z

Source-reported events for the cited work

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Observation 90c64de6-d5e7-444a-87ae-1e9d0e979e2f · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Dataset Augmentation by Mixing Visual Concepts Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.847654Z

Source-reported events for the cited work

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Observation bf81acb3-6aa6-470a-9ed3-e4775c23f529 · outbound

This paper cites Fast autoaugment.

Dataset Augmentation by Mixing Visual Concepts Fast autoaugment

Reference 31

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Source-reported events for the cited work

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Observation dff3f3be-833f-4847-9300-6bf4d97c2418 · outbound

This paper cites Learning deep energy models.

Dataset Augmentation by Mixing Visual Concepts Learning deep energy models

Reference 32

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Source-reported events for the cited work

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Observation 39e20092-3b7e-4355-93a3-faeeda2dff1a · outbound

This paper cites VisDA: The Visual Domain Adaptation Challenge.

Dataset Augmentation by Mixing Visual Concepts VisDA: The Visual Domain Adaptation Challenge

Reference 33

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Source-reported events for the cited work

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Observation 01d5a942-ea90-401f-a3fd-2efe26c12d26 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Dataset Augmentation by Mixing Visual Concepts Learning transferable visual models from natural language supervi- sion

Reference 34

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Source-reported events for the cited work

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Observation c60eae84-29d3-4f40-bbbf-c57df31ee74d · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Dataset Augmentation by Mixing Visual Concepts Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 35

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Source-reported events for the cited work

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Observation 67e16275-2201-45f8-92eb-0305323ee46d · outbound

This paper cites Variational inference with normalizing flows.

Dataset Augmentation by Mixing Visual Concepts Variational inference with normalizing flows

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.767285Z

Source-reported events for the cited work

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Observation c47a5ffe-e35d-45fd-9cdd-2ed44191dfaa · outbound

This paper cites Stochastic backpropagation and approximate inference in deep generative models.

Dataset Augmentation by Mixing Visual Concepts Stochastic backpropagation and approximate inference in deep generative models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.737633Z

Source-reported events for the cited work

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Observation ad97d05c-9d32-4fc0-89a8-4f4d553867a6 · outbound

This paper cites Playing for data: Ground truth from computer games.

Dataset Augmentation by Mixing Visual Concepts Playing for data: Ground truth from computer games

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.716553Z

Source-reported events for the cited work

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Observation 34ee2ba8-043d-4f96-b6fb-63829a16cc39 · outbound

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

Dataset Augmentation by Mixing Visual Concepts High-resolution image synthesis with latent diffusion models

Reference 39

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Observation 12a0fabb-c878-4aa1-81a3-8ded84823584 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Dataset Augmentation by Mixing Visual Concepts U- net: Convolutional networks for biomedical image segmen- tation

Reference 40

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Observation 587ca0f9-0390-4b3e-9425-3bd90b6d85e6 · outbound

This paper cites Imagenet large scale visual recognition challenge.

Dataset Augmentation by Mixing Visual Concepts Imagenet large scale visual recognition challenge

Reference 41

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Source-reported events for the cited work

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Observation b4096f3e-706a-425e-a5e8-0b5feb953e1e · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Dataset Augmentation by Mixing Visual Concepts Photorealistic text-to-image diffusion models with deep language understanding

Reference 42

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Source-reported events for the cited work

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Observation 6b9d1cbc-871a-4a90-9d88-a0115738f2f1 · outbound

This paper cites Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion.

Dataset Augmentation by Mixing Visual Concepts Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion

Reference 43

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Observation 4870fcb3-9802-4a7d-a35f-fb7ef8ead04f · outbound

This paper cites Effective Data Augmentation With Diffusion Models.

Dataset Augmentation by Mixing Visual Concepts Effective Data Augmentation With Diffusion Models

Reference 44

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Observation eb7a9c8b-527a-4cf1-8efd-46bd4579a272 · outbound

This paper cites A bayesian data augmentation approach for learn- ing deep models.

Dataset Augmentation by Mixing Visual Concepts A bayesian data augmentation approach for learn- ing deep models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.633600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 38bc74af-667a-40c8-a778-6720b4a24226 · outbound

This paper cites Conditional image genera- tion with pixelcnn decoders.

Dataset Augmentation by Mixing Visual Concepts Conditional image genera- tion with pixelcnn decoders

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.610154Z

Source-reported events for the cited work

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Observation d3115e80-a282-4d31-8ce5-c97a48479cf6 · outbound

This paper cites Wide residual net- works.

Dataset Augmentation by Mixing Visual Concepts Wide residual net- works

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.591464Z

Source-reported events for the cited work

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Observation 2b30acdb-8667-47c6-b6e0-62fc55afff2a · outbound

This paper cites Datasetgan: Efficient labeled data factory with minimal human effort.

Dataset Augmentation by Mixing Visual Concepts Datasetgan: Efficient labeled data factory with minimal human effort

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:32:39.567841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.