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2026-02-17 14:59:53
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SyntML 2022 HOME PROGRAM SPEAKERS TALKS PANEL ORGANIZERS HOME PROGRAM SPEAKERS TALKS PANEL ORGANIZERS Simulation Generative Models Domain Adaptation Data Augmentation Applications Introduction Modern machine learning models are data hungry. Collecting and annotating data is hard and expensive. Moreover, there is a need for diverse training data in order to create fair and unbiased models. With advances in simulations and generative models, utilizing synthetic data is becoming a viable alternative to training models using real data. However, there are still many open problems to realize this goal, such as lack of standard tools, realism gap between synthetic and real data, and need for machine learning algorithms to effectively utilize imperfect synthetic data. The goal of this workshop is to bring in researchers, and discuss potential issues, current trends, and solutions around utilizing synthetic data to devel...
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