Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:39:25.574270Z
Paper Citation Record · LEDGER
Understanding Trade offs When Conditioning Synthetic Data
As of 18 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2507.02217.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
72 of 72 outbound references displayed
- verified exact10
- verified fuzzy27
- unresolved35
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 24bad93b-7231-4fa8-9222-e82b10f83f42 · outbound
This paper cites Gpt-4 technical report, 2023.
Understanding Trade offs When Conditioning Synthetic Data Gpt-4 technical report, 2023
Reference 1
- Resolution
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- raw_fallback, observed 2026-08-06T20:39:32.417937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1fa5460a-2ea7-4e83-949a-9b8e022accbf · outbound
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 192501a5-7a48-4b48-92b6-14a8bc2456a0 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:32.271250Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 680696a2-4666-40af-a291-ff53e4d97fc3 · outbound
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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d910e2ff-37bf-4ae8-a754-77eceecfd351 · outbound
This paper cites A computational approach to edge detection.
Understanding Trade offs When Conditioning Synthetic Data A computational approach to edge detection
Reference 5
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2d259fed-73e3-4ee1-be79-848fa90c88ec · outbound
This paper cites A computational approach to edge detection.
Understanding Trade offs When Conditioning Synthetic Data A computational approach to edge detection
Reference 6
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eaa04a14-c335-4907-9d48-9b4a13c485df · outbound
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:39:27.156798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 702c7882-ec05-4d02-b54e-97b240856f95 · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 800df193-7fa1-44c2-8df4-ae0c0e2a41b5 · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b086015-38df-4efe-a964-67a61441ea15 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:31.783346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 45057e26-dae1-404f-8b7f-5c8d40256620 · outbound
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:39:27.030716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 41fa1fd7-25d0-43c5-9c64-b1fa7f7c0d4e · outbound
This paper cites an unresolved cited work.
Understanding Trade offs When Conditioning Synthetic Data Unresolved cited work
Reference 12
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:39:31.621505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9c06d696-d836-4c44-9e27-265d5f02b357 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:31.450806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 495c68ee-580d-4889-916e-1533362ccf6b · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:31.285500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 13f02161-cf7d-4c32-bd1e-4f186a5360b5 · outbound
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:39:26.893969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7b59f7c2-f2fd-4524-82c6-396b4ce9de94 · outbound
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
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5751e9ff-f0d3-41f0-9907-7fd8e348983f · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:31.039726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7c792609-2136-439b-a0db-4a07ede3a42e · outbound
This paper cites Mask R-CNN.
Understanding Trade offs When Conditioning Synthetic Data Mask R-CNN
Reference 18
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b467b52a-3662-4c12-b506-37e24a66dce2 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:30.871163Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e0162e79-9667-40b7-b635-f9629c32f698 · outbound
This paper cites Denoising Diffusion Probabilistic Models.
Understanding Trade offs When Conditioning Synthetic Data Denoising Diffusion Probabilistic Models
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:21.111068Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4a43c41-c268-4bf9-abe3-9c1cc6feb43b · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 46308d15-9607-473c-8a2d-40c5055b59ae · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:30.702638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2fc874fe-08cd-4c1b-aa9a-c37a8173feb9 · outbound
This paper cites Ultralytics YOLO, 2023.
Understanding Trade offs When Conditioning Synthetic Data Ultralytics YOLO, 2023
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:30.521540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fc9edcce-7245-4888-bae8-b4fdcd262594 · outbound
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:39:26.640194Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 686df2a1-2010-412d-8c03-0d5cfc48b36f · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:30.336545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fb697080-fb0d-4d66-be5b-2237cc3e03c5 · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:21.788277Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b95f737-9f06-4a34-9a47-6b1d131ba080 · outbound
This paper cites Dataset Enhancement with Instance-Level Augmentations.
Understanding Trade offs When Conditioning Synthetic Data Dataset Enhancement with Instance-Level Augmentations
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:39:26.539133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4c4303fc-a807-4950-a3ac-f10075f77071 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:30.167469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 14953de5-3722-4326-93b4-66528f8f200c · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c4054436-db0e-49e5-aa51-e06f6d01c656 · outbound
This paper cites Lawrence Zitnick.
Understanding Trade offs When Conditioning Synthetic Data Lawrence Zitnick
Reference 31
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3a657d26-32d9-4fb1-a839-ac5402ae6ac4 · outbound
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae515b5c-eca9-489a-8f3f-7b6e9fe0c5c8 · outbound
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Understanding Trade offs When Conditioning Synthetic Data Visual instruction tuning
Reference 33
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88901c7f-c211-4aa3-8c58-f75992c8c65c · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e144cfd4-2722-4634-8cd1-7eb3f5fe9e00 · outbound
This paper cites Decoupled Weight Decay Regularization.
Understanding Trade offs When Conditioning Synthetic Data Decoupled Weight Decay Regularization
Reference 35
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e6c03bf1-de47-4b3d-980e-9cdb730c3e0b · outbound
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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 40a0d8a3-34c3-415d-b760-f8dc84e2c758 · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93317109-d5c3-4692-b5a2-19dac4591b97 · outbound
This paper cites Scaling Open-Vocabulary Object Detection.
Understanding Trade offs When Conditioning Synthetic Data Scaling Open-Vocabulary Object Detection
Reference 38
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 754c22e1-28e8-4142-88d9-f4696e07ea83 · outbound
This paper cites an unresolved cited work.
Understanding Trade offs When Conditioning Synthetic Data Unresolved cited work
Reference 39
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7d36e69e-ec2b-40b0-a47a-f70b16d1c4fe · outbound
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
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 695a2c0e-6435-4823-9cac-dfc91fec528f · outbound
This paper cites Localizing object-level shape variations with text-to-image diffusion models.
Understanding Trade offs When Conditioning Synthetic Data Localizing object-level shape variations with text-to-image diffusion models
Reference 41
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5a3d6ea-10b4-4aa3-9966-af57197d4c4d · outbound
This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023.
Understanding Trade offs When Conditioning Synthetic Data Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:29.355284Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b4b2a65e-7bdc-4546-aa34-5c29103f5b4c · outbound
This paper cites Meta-learning with implicit gradients.
Understanding Trade offs When Conditioning Synthetic Data Meta-learning with implicit gradients
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:29.176141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dc46b9ba-9d31-4594-a940-79364e2c3fed · outbound
This paper cites Zero-shot text-to-image generation.
Understanding Trade offs When Conditioning Synthetic Data Zero-shot text-to-image generation
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:23.365116Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9a257532-f8a3-4355-83fc-658783e6bc0f · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:23.451279Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b235714f-f8aa-4c24-901a-e594e082a5b9 · outbound
This paper cites YOLO9000: Better, Faster, Stronger.
Understanding Trade offs When Conditioning Synthetic Data YOLO9000: Better, Faster, Stronger
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:23.491195Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 66692a97-c228-4dcf-a1d5-1147d5cfb754 · outbound
This paper cites YOLOv3: An Incremental Improvement.
Understanding Trade offs When Conditioning Synthetic Data YOLOv3: An Incremental Improvement
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:23.597200Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60c4225a-919b-4026-a0a8-8ebfb5af5893 · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:23.687482Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 243bbacb-383d-42bd-a619-e7a9dc5e63be · outbound
This paper cites Real-Time Flying Object Detection with YOLOv8.
Understanding Trade offs When Conditioning Synthetic Data Real-Time Flying Object Detection with YOLOv8
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:23.835177Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7345dbcf-18ea-4ba8-bf38-53a36ac2da89 · outbound
This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.
Understanding Trade offs When Conditioning Synthetic Data High-Resolution Image Synthesis with Latent Diffusion Models
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:23.936345Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2ad1d5fb-1804-4046-b36a-4da489234c38 · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a6c2f10d-2f94-4af6-a302-61d319ac4425 · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:24.077254Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d59562c4-c9c9-4d34-858e-ed94d1e59b8b · outbound
This paper cites Meta-learning with memory-augmented neural networks.
Understanding Trade offs When Conditioning Synthetic Data Meta-learning with memory-augmented neural networks
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:29.016599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:39:26.217186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f2ff70de-26ee-482c-bc7e-c0ed028e55e7 · outbound
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
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T20:39:26.043285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:24.348987Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a067e855-270e-40ee-8925-50eb4da19b20 · outbound
This paper cites Denoising Diffusion Implicit Models.
Understanding Trade offs When Conditioning Synthetic Data Denoising Diffusion Implicit Models
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:24.439186Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dbd94184-8835-4582-bb88-157ad0478af1 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:39:28.848198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:24.620212Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:39:25.775023Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:39:25.029975Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.