Current AI systems often need extensive amounts of data for training, resulting in time-consuming processes, increased expenses, and negative environmental impacts. Therefore, new data-efficient models and algorithms which can achieve high performance with small amounts of training data should be developed. This project aims to explore established techniques and develop new methods to improve the data efficiency of future AI systems.

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Sebastian Bast