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pro vyhledávání: '"Sierak, Paulina"'
In many manufacturing settings, annotating data for machine learning and computer vision is costly, but synthetic data can be generated at significantly lower cost. Substituting the real-world data with synthetic data is therefore appealing for many
Externí odkaz:
http://arxiv.org/abs/2406.19175
Publications proposing novel machine learning methods are often primarily rated by exhibited predictive performance on selected problems. In this position paper we argue that predictive performance alone is not a good indicator for the worth of a pub
Externí odkaz:
http://arxiv.org/abs/2406.03980