Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting | Paper Citation Record

Observation e1355a76-1825-4b92-800c-a24a321c0c1e · outbound

This paper cites Toward understanding generative data augmentation.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Toward understanding generative data augmentation

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This paper cites Fake it till you make it: Learning transferable representations from synthetic imagenet clones.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Fake it till you make it: Learning transferable representations from synthetic imagenet clones

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Unresolved cited work

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This paper cites Is synthetic data from generative models ready for image recognition? In The Eleventh International Conference on Learning Representations, 2023.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Is synthetic data from generative models ready for image recognition? In The Eleventh International Conference on Learning Representations, 2023

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This paper cites IS SYNTHETIC DATA USEFUL FOR TRANSFER LEARNING? AN INVESTI- GATION INTO DATA GENERATION, VOLUME, AND UTILIZATION, 2024.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting IS SYNTHETIC DATA USEFUL FOR TRANSFER LEARNING? AN INVESTI- GATION INTO DATA GENERATION, VOLUME, AND UTILIZATION, 2024

Reference 5

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This paper cites Scaling laws of synthetic images for model training.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Scaling laws of synthetic images for model training

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Observation 7fc2659c-c68a-4188-9145-e1a3c13844d4 · outbound

This paper cites Datadream: Few-shot guided dataset generation.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Datadream: Few-shot guided dataset generation

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This paper cites Real-fake: Effective training data synthesis through distribution matching, 2024.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Real-fake: Effective training data synthesis through distribution matching, 2024

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This paper cites Test-time Alignment of Diffusion Models without Reward Over-optimization.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Test-time Alignment of Diffusion Models without Reward Over-optimization

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Observation 06efc3cc-0267-4901-b6ac-ac0bc43f367e · outbound

This paper cites Diffusion curriculum: Synthetic-to-real data curriculum via image-guided diffusion.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Diffusion curriculum: Synthetic-to-real data curriculum via image-guided diffusion

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Observation 3f74dd41-0a71-4898-8edb-bcea2b497560 · outbound

This paper cites Increasing the utility of synthetic images through chamfer guidance.arXiv preprint arXiv:2508.10631, 2025.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Increasing the utility of synthetic images through chamfer guidance.arXiv preprint arXiv:2508.10631, 2025

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This paper cites Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency

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Observation 09e81351-233f-4430-b173-7485236a1047 · outbound

This paper cites Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion

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Observation b68d655a-4db1-417c-b6b5-bc9d5a5eccd5 · outbound

This paper cites Data augmentation for image classification using generative ai.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Data augmentation for image classification using generative ai

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Observation 4b8b0bfc-671c-4503-8fe2-8934afddcbba · outbound

This paper cites Gonzalez, and Trevor Darrell.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Gonzalez, and Trevor Darrell

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This paper cites Datacomp: In search of the next generation of multimodal datasets.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Datacomp: In search of the next generation of multimodal datasets

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This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting SemDeDup: Data-efficient learning at web-scale through semantic deduplication

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Observation 06ff4a81-3f80-4af4-bbf7-d9e8bd2bfd74 · outbound

This paper cites Effective audio classification network based on paired inverse pyramid structure and dense mlp block.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Effective audio classification network based on paired inverse pyramid structure and dense mlp block

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Observation fac906f7-8c75-4b0f-8e54-81e8370727ac · outbound

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

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Datasetgan: Efficient labeled data factory with minimal human effort

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Observation be454cb5-4a19-43a4-a95e-b86a4b77227c · outbound

This paper cites Improved precision and recall metric for assessing generative models.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Improved precision and recall metric for assessing generative models

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Observation 20d55aa3-9361-4426-ab66-b4a554f74074 · outbound

This paper cites Explore the power of synthetic data on few-shot object detection.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Explore the power of synthetic data on few-shot object detection

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This paper cites Aerogen: Enhancing remote sensing object detection with diffusion-driven data generation.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Aerogen: Enhancing remote sensing object detection with diffusion-driven data generation

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This paper cites Noise-consistent siamese-diffusion for medical image synthesis and segmentation.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Noise-consistent siamese-diffusion for medical image synthesis and segmentation

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This paper cites Domain gap embeddings for generative dataset augmentation.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Domain gap embeddings for generative dataset augmentation

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Observation 64a7a392-e6e5-4735-a384-1e2a5a38a417 · outbound

This paper cites Training on Thin Air: Improve Image Classification with Generated Data.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Training on Thin Air: Improve Image Classification with Generated Data

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This paper cites StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners

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This paper cites SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

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This paper cites Learning Vision from Models Rivals Learning Vision from Data.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Learning Vision from Models Rivals Learning Vision from Data

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Observation 5c857f26-fb2b-4a17-9f99-fa7c40d431a4 · outbound

This paper cites Contrastive Learning with Synthetic Positives.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Contrastive Learning with Synthetic Positives

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This paper cites From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition

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This paper cites Will large-scale generative models corrupt future datasets? In2023 IEEE/CVF International Conference on Computer Vision (ICCV), page 20498–20508.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Will large-scale generative models corrupt future datasets? In2023 IEEE/CVF International Conference on Computer Vision (ICCV), page 20498–20508

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This paper cites Do generated data always help contrastive learning?,.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Do generated data always help contrastive learning?,

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Observation 40335866-76d5-4e75-a5ad-f1c1cf3dd289 · outbound

This paper cites Do Generated Data Always Help Contrastive Learning?.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Do Generated Data Always Help Contrastive Learning?

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This paper cites Utilgen: Utility-centric generative data augmentation with dual-level task adaptation.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Utilgen: Utility-centric generative data augmentation with dual-level task adaptation

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This paper cites Effective pruning of web-scale datasets based on complexity of concept clusters.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Effective pruning of web-scale datasets based on complexity of concept clusters

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This paper cites Learning what matters: Prioritized concept learning via relative error-driven sample selection.arXiv preprint arXiv:2506.01085, 2025.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Learning what matters: Prioritized concept learning via relative error-driven sample selection.arXiv preprint arXiv:2506.01085, 2025

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Sampling strategies for gan synthetic data

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

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Datasetgan: Efficient labeled data factory with minimal human effort

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Data aug- mentation for environmental sound classification using diffusion probabilistic model with top-k selection discriminator

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This paper cites Strata: Self-training with task augmentation for better few-shot learning.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Strata: Self-training with task augmentation for better few-shot learning

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This paper cites URL http://dx.doi.org/10.18653/v1/ 2021.emnlp-main.462.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting URL http://dx.doi.org/10.18653/v1/ 2021.emnlp-main.462

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Observation 75a5b133-6f3f-4530-8fd5-34acb5d86302 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Learning transferable visual models from natural language supervision

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This paper cites A Training-free Synthetic Data Selection Method for Semantic Segmentation.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting A Training-free Synthetic Data Selection Method for Semantic Segmentation

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This paper cites Reliable fidelity and diversity metrics for generative models, 2020.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Reliable fidelity and diversity metrics for generative models, 2020

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This paper cites Deep data augmentation for weed recognition enhancement: A diffusion probabilistic model and transfer learning based approach.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Deep data augmentation for weed recognition enhancement: A diffusion probabilistic model and transfer learning based approach

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Diversified in-domain synthesis with efficient fine-tuning for few-shot classification

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Effective data augmentation with diffusion models

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Feedback-guided Data Synthesis for Imbalanced Classification

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This paper cites An Empirical Study of Training Self-Supervised Vision Transformers.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting An Empirical Study of Training Self-Supervised Vision Transformers

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This paper cites Cifar-10 (canadian institute for advanced research).URL http://www.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Cifar-10 (canadian institute for advanced research).URL http://www

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This paper cites Better diffusion models further improve adversarial training.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Better diffusion models further improve adversarial training

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This paper cites Analyzing and improving the training dynamics of diffusion models.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Analyzing and improving the training dynamics of diffusion models

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This paper cites Guiding a diffusion model with a bad version of itself.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Guiding a diffusion model with a bad version of itself

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This paper cites Contrastive multiview coding.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Contrastive multiview coding

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This paper cites Fake It Till You Make It: Face analysis in the wild using synthetic data alone.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Fake It Till You Make It: Face analysis in the wild using synthetic data alone

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Extracting training data from diffusion models

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Dcface: Synthetic face generation with dual condition diffusion model

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This paper cites Improving geo-diversity of generated images with contextualized vendi score guidance.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Improving geo-diversity of generated images with contextualized vendi score guidance

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Jodiffusion: Jointly diffusing image with pixel-level annotations for semantic segmentation promotion

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Deep residual learning for image recognition

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Efficientnet: Rethinking model scaling for convolutional neural networks

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This paper cites Visual transformers: Token-based image representation and processing for computer vision, 2020.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Visual transformers: Token-based image representation and processing for computer vision, 2020

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Benchmarking neural network robustness to common corruptions and perturbations

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This paper cites Do ImageNet Classifiers Generalize to ImageNet?.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Do ImageNet Classifiers Generalize to ImageNet?

Reference 68

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Learning robust global represen- tations by penalizing local predictive power

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This paper cites Benchmarking neural network robustness to common corruptions and perturbations.Proceedings of the International Conference on Learning Representations, 2019.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Benchmarking neural network robustness to common corruptions and perturbations.Proceedings of the International Conference on Learning Representations, 2019

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Imagenet-cartoon and imagenet-drawing: two domain shift datasets for imagenet

Reference 71

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Cifar- 10-warehouse: Broad and more realistic testbeds in model generalization analysis

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Encoder-decoder with atrous separable convolution for semantic image segmentation

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Training generative adversarial networks with limited data

Reference 74

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Elucidating the design space of diffusion-based generative models

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Sigmoid loss for language image pre-training, 2023

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Unresolved cited work

Reference 77

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting near” set contains canonical patterns, while the “far

Reference 78

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Such property implies that learning the pattern in HO, and then we can reconstruct the whole original feature space with the smallest cost

Reference 79

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting $NUM_GPU

Reference 80

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Unresolved cited work

Reference 81

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Reference 82

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Unresolved cited work

Reference 83

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Reference 84

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting common_image

Reference 85

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Unresolved cited work

Reference 86

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This paper cites As vision-language models continue to advance,13 synthetic datasets that support multimodal training are becoming increasingly important.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting As vision-language models continue to advance,13 synthetic datasets that support multimodal training are becoming increasingly important

Reference 87

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting In practice, however, only a small19 reference set is often sufficient, since the partition depends more on relative intra-class similarity than20 on absolute data scale.21

Reference 88

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Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Although Fig

Reference 89

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