Performance evaluation of GANs in a semi-supervised OCR use case

Even in the age of big data labelled data is a scarce resource in many machine learning use cases. We evaluate generative adversarial networks (GANs) at the task of extracting information from vehicle registrations under a varying amount of labelled data and compare the performance with supervised learning techniques. Using unlabelled data shows a significant improvement.

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Bridging the Gap: from Data Science to Production

A recent but quite common observation in industry is that although there is an overall high adoption of data science, many companies struggle to get it into production. Huge teams of well-payed data scientists often present one fancy model after the other to their managers but their proof of concepts …

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“Which car fits my life?” - mobile.de’s approach to recommendations

At mobile.de, Germany’s biggest car marketplace, a dedicated team of data engineers and scientists, supported by the IT project house inovex is responsible for creating intelligent data products. Driven by our company slogan “Find the car that fits your life”, we focus on personalised recommendations to address several …

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