Image recognition, reconstruction – and how AI can go wrong

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Images and artificial intelligence

You don’t have to be a computer geek to know that artificial intelligence (AI) is having a moment in the sun just now. 

In recent years, we have made huge progress in the field of AI, and particularly through deep learning algorithms. These algorithms have been around for many years, but are now becoming much more useful thanks to the exponential growth in both data and computing power to handle them.

Deep learning algorithms are particularly useful for analysing images. For example, in the images above, they have been used to classify the first image as a person, segment the second into person/background, and recognise objects in the third. 

One specific type of deep learning algorithm is Generative Adversarial Networks or GANs. These use two deep learning networks: the first generates new images, and the second distinguishes the new (fake) from real images. This gallery discusses some uses for GANs, and where they don’t do so well. Welcome to the good, the bad and the ugly. 


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Veronique Van Vlasselaer

Data science enthusiast with an infinite passion to operationalize business decisions supported by insights from data analysis, machine learning and AI; anti-fraud expert; team player who loves to work with inspiring people, main focus = having fun on the job

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