mlsanityData to 7 Oct 2026

SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

arXiv 2402.01832 ↗First posted 2 Feb 2024cs.CV cs.AI cs.LGcited by 64

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Abstract (arXiv)

We present SynthCLIP, a CLIP model trained on entirely synthetic text-image pairs. Leveraging recent text-to-image (TTI) networks and large language models (LLM), we generate synthetic datasets of images and corresponding captions at scale, with no human intervention. In this work, we provide an analysis on CLIP models trained on synthetic data. We provide insights on the data generation strategy, number of samples required, scaling trends, and resulting properties. We also introduce SynthCI-30M, a purely synthetic dataset comprising 30 million captioned images. Our code, trained models, and data, are released as open source at https://github.com/hammoudhasan/SynthCLIP

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). Builds on 61 of its 78 references that are in the map. First posted on arXiv 2 Feb 2024.

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61 of 78 references.

References · 61

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38 more
  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  2. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
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  19. 2021CLIPort: What and Where Pathways for Robotic Manipulationcited by 617
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  22. 2017Learning from Synthetic Humanscited by 358
  23. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 326
  24. 2022Is synthetic data from generative models ready for image recognition?cited by 320
  25. 2023Demystifying CLIP Datacited by 265
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50 most cited of 61 All 61 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 31; 2025: 21; 2026: 12.

64 papers.

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  1. 2024An Introduction to Vision-Language Modelingcited by 175
  2. 2024No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performancecited by 75
  3. 2024DreamLIP: Language-Image Pre-training with Long Captionscited by 72
  4. 2025Vision-Language Models Do Not Understand Negationcited by 69
  5. 2024Latent Guard: a Safety Framework for Text-to-image Generationcited by 68
  6. 2024DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curationcited by 64
  7. 2024TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negativescited by 57
  8. 2024FLAIR: VLM with Fine-grained Language-informed Image Representationscited by 47
  9. 2024Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Imagescited by 41
  10. 2024TIPS: Text-Image Pretraining with Spatial awarenesscited by 37
  11. 2024DataDream: Few-shot Guided Dataset Generationcited by 28
  12. 2024LoTLIP: Improving Language-Image Pre-training for Long Text Understandingcited by 27
38 more
  1. 2024A Practitioner's Guide to Continual Multimodal Pretrainingcited by 22
  2. 2025MobileCLIP2: Improving Multi-Modal Reinforced Trainingcited by 20
  3. 2024The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Bettercited by 19
  4. 2024SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Datacited by 14
  5. 2024Detecting Human Artifacts from Text-to-Image Modelscited by 12
  6. 2024SteerDiff: Steering towards Safe Text-to-Image Diffusion Modelscited by 10
  7. 2024Personalized Representation from Personalized Generationcited by 9
  8. 2025PromptMap: An Alternative Interaction Style for AI-Based Image Generationcited by 8
  9. 2024On Pretraining Data Diversity for Self-Supervised Learningcited by 7
  10. 2024Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captionscited by 7
  11. 2025Seeing What Matters: Empowering CLIP with Patch Generation-to-Selectioncited by 7
  12. 2025FIX-CLIP: Dual-Branch Hierarchical Contrastive Learning via Synthetic Captions for Better Understanding of Long Textcited by 7
  13. 2024Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?cited by 6
  14. 2026When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generatorscited by 6
  15. 2024Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Datacited by 5
  16. 2024Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Datacited by 4
  17. 2024Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIPcited by 4
  18. 2025Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Datacited by 4
  19. 2025SuperCLIP: CLIP with Simple Classification Supervisioncited by 4
  20. 2024LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Textscited by 3
  21. 2025CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized Captionscited by 3
  22. 2025SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioningcited by 3
  23. 2025Training-Free Synthetic Data Generation with Dual IP-Adapter Guidancecited by 3
  24. 2026Grounding Synthetic Data Generation With Vision and Language Modelscited by 3
  25. 2024Exploring the Spectrum of Visio-Linguistic Compositionality and Recognitioncited by 2
  26. 2024MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representationscited by 2
  27. 2025DiffCLIP: Differential Attention Meets CLIPcited by 2
  28. 2025Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasetscited by 2
  29. 2025LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learningcited by 2
  30. 2025Learning complete and explainable visual representations from itemized text supervisioncited by 2
  31. 2026Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridgingcited by 2
  32. 2024Precision at Scale: Domain-Specific Datasets On-Demandcited by 1
  33. 2024Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models?cited by 1
  34. 2025LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidancecited by 1
  35. 2025Multimodal Representation Learning Conditioned on Semantic Relationscited by 1
  36. 2025Text-Printed Image: Bridging the Image-Text Modality Gap for Text-centric Training of Large Vision-Language Modelscited by 1
  37. 2025Semantic search for 100M+ galaxy images using AI-generated captionscited by 1
  38. 2026PolyGen: Fully Synthetic Vision-Language Training via Multi-Generator Ensemblescited by 1

50 most cited of 64 All 64 (JSON)