The paper introduces the Real‑Calibrated Synthetic‑First Data Engine, a modular pipeline that integrates controllable diffusion‑based synthetic image generation with multi‑stage curation, filtering, and optional uncertainty‑driven selection and human verification. Designed as a CLI‑based framework, it allows independent configuration of generation, filtering, selection, and validation modules to enhance reproducibility and flexibility in real‑world data workflows. Empirical tests on human pose estimation demonstrate that synthetic data can boost a real‑data baseline when used as low‑cost augmentation, though synthetic‑only training still lags behind real‑only performance, underscoring the importance of data‑centric orchestration in low‑data regimes.
By Yukang Shen, Zhiguo Liu, Yingshu Li, Yan Huang
arXiv:2607. 02637v1 Announce Type: cross Abstract: Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models.
By Disheng Liu, Tuo Liang, Chaoda Song, Yu Yin
arXiv:2510. 06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data.
By Ayush Zenith, Arnold Zumbrun, Neel Raut, Jing Lin
JEPAMatch introduces a new semi‑supervised learning framework that replaces traditional output‑thresholding with explicit geometric shaping of latent representations. By combining the FlexMatch loss with a latent‑space regularization inspired by LeJEPA, the method encourages isotropic Gaussian structure in the embedding space, mitigating class imbalance and noisy pseudo‑labels. Experiments on CIFAR‑100, STL‑10, and Tiny‑ImageNet show consistent performance gains and faster convergence compared to existing FixMatch‑based baselines.
By Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini
Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as FID, KID, IS, LPIPS and SSIM that measure structural or distributional similarity.
arXiv:2606. 25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs.
By \"Umit Mert \c{C}a\u{g}lar, Alptekin Temizel