engleski

Elements of Learning Algorithms for Natural Scene Understanding

Deep learning has led to unprecedented improvement of computer vision, natural language processing and other fields of artificial intelligence. However, our models still underperform on unusual and adversarial test examples, while offering limited interpretability and explainability. Nevertheless, experienced practitioners seldom regard their models as black boxes. Instead, they promote desired behaviour through suitable kinds of inductive bias and careful exploitation of available data. I will illustrate these concepts by describing elements of learning algorithms which have been extensively exploited within my research group in the past few years. The second part of my talk will describe our ongoing collaborations with the local industry. I will point out advantages of such arrangements for all involved parties. The talk will conclude with a brief overview of current challenges and opportunities in our field.

Deep learning, dense prediction

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