Understanding Trade offs When Conditioning Synthetic Data | Paper Citation Record

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This paper cites Gpt-4 technical report, 2023.

Understanding Trade offs When Conditioning Synthetic Data Gpt-4 technical report, 2023

Reference 1

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

Understanding Trade offs When Conditioning Synthetic Data Synthetic Data from Diffusion Models Improves ImageNet Classification

Reference 2

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This paper cites Label-efficient se- mantic segmentation with diffusion models, 2022.

Understanding Trade offs When Conditioning Synthetic Data Label-efficient se- mantic segmentation with diffusion models, 2022

Reference 3

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This paper cites Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection.

Understanding Trade offs When Conditioning Synthetic Data Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection

Reference 4

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This paper cites A computational approach to edge detection.

Understanding Trade offs When Conditioning Synthetic Data A computational approach to edge detection

Reference 5

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This paper cites A computational approach to edge detection.

Understanding Trade offs When Conditioning Synthetic Data A computational approach to edge detection

Reference 6

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This paper cites Combating noisy labels in object detection datasets.

Understanding Trade offs When Conditioning Synthetic Data Combating noisy labels in object detection datasets

Reference 7

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This paper cites RandAugment: Practical automated data augmentation with a reduced search space.

Understanding Trade offs When Conditioning Synthetic Data RandAugment: Practical automated data augmentation with a reduced search space

Reference 8

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This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

Understanding Trade offs When Conditioning Synthetic Data Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 9

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

Understanding Trade offs When Conditioning Synthetic Data Imagenet: A large-scale hierarchical image database

Reference 10

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This paper cites Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation.

Understanding Trade offs When Conditioning Synthetic Data Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation

Reference 11

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Understanding Trade offs When Conditioning Synthetic Data Unresolved cited work

Reference 12

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This paper cites Instagen: Enhancing object detection by training on syn- thetic dataset.

Understanding Trade offs When Conditioning Synthetic Data Instagen: Enhancing object detection by training on syn- thetic dataset

Reference 13

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This paper cites Instructdiffusion: A generalist modeling inter- face for vision tasks.

Understanding Trade offs When Conditioning Synthetic Data Instructdiffusion: A generalist modeling inter- face for vision tasks

Reference 14

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This paper cites On Pretraining Data Diversity for Self-Supervised Learning.

Understanding Trade offs When Conditioning Synthetic Data On Pretraining Data Diversity for Self-Supervised Learning

Reference 15

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This paper cites Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting.

Understanding Trade offs When Conditioning Synthetic Data Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting

Reference 16

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This paper cites Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment.

Understanding Trade offs When Conditioning Synthetic Data Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment

Reference 17

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This paper cites Mask R-CNN.

Understanding Trade offs When Conditioning Synthetic Data Mask R-CNN

Reference 18

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This paper cites IS SYN- THETIC DATA FROM GENERATIVE MODELS READY FOR IMAGE RECOGNITION? In The Eleventh Interna- tional Conference on Learning Representations, 2023.

Understanding Trade offs When Conditioning Synthetic Data IS SYN- THETIC DATA FROM GENERATIVE MODELS READY FOR IMAGE RECOGNITION? In The Eleventh Interna- tional Conference on Learning Representations, 2023

Reference 19

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Observation 74804412-bf6c-4887-a864-99a1c4d45e03 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Understanding Trade offs When Conditioning Synthetic Data Classifier-Free Diffusion Guidance

Reference 20

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This paper cites Denoising Diffusion Probabilistic Models.

Understanding Trade offs When Conditioning Synthetic Data Denoising Diffusion Probabilistic Models

Reference 21

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Understanding Trade offs When Conditioning Synthetic Data LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

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This paper cites Task agnos- tic meta-learning for few-shot learning.

Understanding Trade offs When Conditioning Synthetic Data Task agnos- tic meta-learning for few-shot learning

Reference 23

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This paper cites Ultralytics YOLO, 2023.

Understanding Trade offs When Conditioning Synthetic Data Ultralytics YOLO, 2023

Reference 24

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This paper cites Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing.

Understanding Trade offs When Conditioning Synthetic Data Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing

Reference 25

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This paper cites Berg, Wan-Yen Lo, Piotr Dollár, and Ross B.

Understanding Trade offs When Conditioning Synthetic Data Berg, Wan-Yen Lo, Piotr Dollár, and Ross B

Reference 26

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This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Understanding Trade offs When Conditioning Synthetic Data Overcoming catastrophic forgetting in neu- ral networks

Reference 27

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This paper cites Dataset Enhancement with Instance-Level Augmentations.

Understanding Trade offs When Conditioning Synthetic Data Dataset Enhancement with Instance-Level Augmentations

Reference 28

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This paper cites Controlnet ++: Improving conditional controls with efficient consistency feedback.

Understanding Trade offs When Conditioning Synthetic Data Controlnet ++: Improving conditional controls with efficient consistency feedback

Reference 29

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This paper cites Gligen: Open-set grounded text-to-image generation.

Understanding Trade offs When Conditioning Synthetic Data Gligen: Open-set grounded text-to-image generation

Reference 30

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This paper cites Lawrence Zitnick.

Understanding Trade offs When Conditioning Synthetic Data Lawrence Zitnick

Reference 31

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This paper cites Improved baselines with visual instruction tuning, 2023.

Understanding Trade offs When Conditioning Synthetic Data Improved baselines with visual instruction tuning, 2023

Reference 32

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Understanding Trade offs When Conditioning Synthetic Data Visual instruction tuning

Reference 33

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This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Understanding Trade offs When Conditioning Synthetic Data Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 34

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Understanding Trade offs When Conditioning Synthetic Data Decoupled Weight Decay Regularization

Reference 35

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This paper cites The effect of improving annotation quality on object detection datasets: A preliminary study.

Understanding Trade offs When Conditioning Synthetic Data The effect of improving annotation quality on object detection datasets: A preliminary study

Reference 36

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This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

Understanding Trade offs When Conditioning Synthetic Data SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 37

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Understanding Trade offs When Conditioning Synthetic Data Scaling Open-Vocabulary Object Detection

Reference 38

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Understanding Trade offs When Conditioning Synthetic Data Unresolved cited work

Reference 39

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This paper cites Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning.

Understanding Trade offs When Conditioning Synthetic Data Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning

Reference 40

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Understanding Trade offs When Conditioning Synthetic Data Localizing object-level shape variations with text-to-image diffusion models

Reference 41

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Understanding Trade offs When Conditioning Synthetic Data Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023

Reference 42

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Understanding Trade offs When Conditioning Synthetic Data Meta-learning with implicit gradients

Reference 43

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This paper cites Zero-shot text-to-image generation.

Understanding Trade offs When Conditioning Synthetic Data Zero-shot text-to-image generation

Reference 44

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This paper cites Hierarchical text-conditional image gener- ation with clip latents, 2022.

Understanding Trade offs When Conditioning Synthetic Data Hierarchical text-conditional image gener- ation with clip latents, 2022

Reference 45

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Understanding Trade offs When Conditioning Synthetic Data YOLO9000: Better, Faster, Stronger

Reference 46

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Understanding Trade offs When Conditioning Synthetic Data YOLOv3: An Incremental Improvement

Reference 47

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This paper cites You Only Look Once: Unified, Real-Time Object Detection.

Understanding Trade offs When Conditioning Synthetic Data You Only Look Once: Unified, Real-Time Object Detection

Reference 48

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Understanding Trade offs When Conditioning Synthetic Data Real-Time Flying Object Detection with YOLOv8

Reference 49

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Understanding Trade offs When Conditioning Synthetic Data High-Resolution Image Synthesis with Latent Diffusion Models

Reference 50

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This paper cites Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi.

Understanding Trade offs When Conditioning Synthetic Data Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi

Reference 51

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

Understanding Trade offs When Conditioning Synthetic Data Photorealistic text-to-image diffusion models with deep language understanding

Reference 52

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Understanding Trade offs When Conditioning Synthetic Data Meta-learning with memory-augmented neural networks

Reference 53

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Observation 0863883e-fad4-4aed-81f0-3578d8c636b4 · outbound

This paper cites Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation.

Understanding Trade offs When Conditioning Synthetic Data Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation

Reference 54

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This paper cites How ford uses ai for quality control.

Understanding Trade offs When Conditioning Synthetic Data How ford uses ai for quality control

Reference 55

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Observation beb921f9-341c-4880-8a30-4ef67e819527 · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Understanding Trade offs When Conditioning Synthetic Data Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 56

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This paper cites Denoising Diffusion Implicit Models.

Understanding Trade offs When Conditioning Synthetic Data Denoising Diffusion Implicit Models

Reference 57

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This paper cites Fsce: Few-shot object detection via contrastive pro- posal encoding.

Understanding Trade offs When Conditioning Synthetic Data Fsce: Few-shot object detection via contrastive pro- posal encoding

Reference 58

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Observation 271b50a6-4942-4f27-943a-62d1a8f209c6 · outbound

This paper cites Gen2Det: Generate to Detect.

Understanding Trade offs When Conditioning Synthetic Data Gen2Det: Generate to Detect

Reference 59

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Observation ad993b3a-752b-4756-9715-2f249e962fef · outbound

This paper cites Effective data augmentation with diffu- sion models.

Understanding Trade offs When Conditioning Synthetic Data Effective data augmentation with diffu- sion models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:39:28.671265Z

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

Observation 86153594-0958-455d-b298-8007fa346e54 · outbound

This paper cites Magic: Multi-modality guided image completion.

Understanding Trade offs When Conditioning Synthetic Data Magic: Multi-modality guided image completion

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:39:28.485495Z

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Observation 448d43d7-841c-4865-8f9d-5089ffb89911 · outbound

This paper cites Investigating prompt engineering in diffusion models, 2022.

Understanding Trade offs When Conditioning Synthetic Data Investigating prompt engineering in diffusion models, 2022

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:39:28.312610Z

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Observation 3d3ba372-c395-4d81-85a3-c03ae11ea2da · outbound

This paper cites DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models.

Understanding Trade offs When Conditioning Synthetic Data DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models

Reference 63

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Observation ac5377db-b222-49ba-a7aa-422770436751 · outbound

This paper cites Meta-rcnn: Meta learning for few-shot object detection.

Understanding Trade offs When Conditioning Synthetic Data Meta-rcnn: Meta learning for few-shot object detection

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:39:28.153416Z

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Observation 1a398493-b803-4728-8c9d-13c43d1f803b · outbound

This paper cites Scaling Robot Learning with Semantically Imagined Experience.

Understanding Trade offs When Conditioning Synthetic Data Scaling Robot Learning with Semantically Imagined Experience

Reference 65

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Observation 215791eb-4f06-4e84-a519-0f87dfdbfc2f · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

Understanding Trade offs When Conditioning Synthetic Data DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T20:39:25.114871Z

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Observation e26a57a7-af9b-4f4c-8a4d-a372b390d98e · outbound

This paper cites Adding conditional control to text-to-image diffusion models, 2023.

Understanding Trade offs When Conditioning Synthetic Data Adding conditional control to text-to-image diffusion models, 2023

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:39:28.015329Z

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

Observation a004189a-9b45-4168-81a8-4298e7317e4e · outbound

This paper cites Rethinking pre- training and self-training.

Understanding Trade offs When Conditioning Synthetic Data Rethinking pre- training and self-training

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:39:27.875387Z

Source-reported events for the cited work

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Observation bb34534d-22fa-4af9-92a7-3ce53765dbaf · outbound

This paper cites These datasets are chosen to span a representative set of tasks that re- searchers and practitioners use when training and evaluat- ing object detection models.

Understanding Trade offs When Conditioning Synthetic Data These datasets are chosen to span a representative set of tasks that re- searchers and practitioners use when training and evaluat- ing object detection models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:39:27.675423Z

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 f9da5b30-eda2-454d-9ce5-c81d2dd56153 · outbound

This paper cites g e n e r a t i o n prompt.

Understanding Trade offs When Conditioning Synthetic Data g e n e r a t i o n prompt

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:39:27.499501Z

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 9e075bca-2890-4d1f-93a0-404687e91da7 · outbound

This paper cites an unresolved cited work.

Understanding Trade offs When Conditioning Synthetic Data Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:39:27.276899Z

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 538f8777-9500-46a4-a50c-c06bd4e94b48 · outbound

This paper cites an unresolved cited work.

Understanding Trade offs When Conditioning Synthetic Data Unresolved cited work

Reference 2014

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:39:29.846264Z

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.

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