mlsanityData to 7 Oct 2026

SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems

arXiv 2308.00994 ↗First posted 2 Aug 2023cs.CV cs.LGcited by 12

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Abstract (arXiv)

Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical. Inspired by recent developments in generative models, this paper explores the potential of synthetic data to address the data imbalance problem. To be specific, our method, dubbed SYNAuG, leverages synthetic data to equalize the unbalanced distribution of training data. Our experiments demonstrate that, although a domain gap between real and synthetic data exists, training with SYNAuG followed by fine-tuning with a few real samples allows to achieve impressive performance on diverse tasks with different data imbalance issues, surpassing existing task-specific methods for the same purpose.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 79 of its 97 references that are in the map. First posted on arXiv 2 Aug 2023.

Builds on

79 of 97 references.

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50 most cited of 79 All 79 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 7; 2026: 3.

12 papers.

  1. 2025SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentationcited by 3
  2. 2023Training on Synthetic Data Beats Real Data in Multimodal Relation Extractioncited by 2
  3. 2025Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classificationcited by 2
  4. 2024Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Groundingcited by 1
  5. 2025T2ID-CAS: Diffusion Model and Class Aware Sampling to Mitigate Class Imbalance in Neck Ultrasound Anatomical Landmark Detectioncited by 1
  6. 2025LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidancecited by 1
  7. 2025Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosiscited by 1
  8. 2025CytoDiff: AI-Driven Cytomorphology Image Synthesis for Medical Diagnosticscited by 1
  9. 2025Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reorderingcited by 0
  10. 2026On Privacy in Data-Space Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metricscited by 0
  11. 2026Gen2Balance: Generative Balancing for Long-Tailed Video Action Recognitioncited by 0
  12. 2026Domain generalization and synthetic data in object detection: the enabler, the probe, and the gapcited by 0