Research
152d agoBalancing Synthetic and Real Data
Apple researchers present a theoretical framework that quantifies how much synthetic data should replace real data to optimize machine‑learning accuracy. By tying algorithmic stability to Wasserstein distance, the study pinpoints an optimal synthetic‑to‑real ratio that reduces test error worldwide.
The Signal
The approach promises more robust models for industries that lack large labeled datasets.