Siniša Šegvić completed his PhD degree in 2004 at the University of Zagreb. He spent one year as a postdoc researcher at IRISA Rennes (2006) and another year as a postdoc researcher at TU Graz (2007). Currently he is a full professor at the University of Zagreb. His research expertise is in the fields of computer vision and deep learning, with special interests in natural scene understanding for autonomous vehicles and safe traffic.
He is a reviewer at top conferences of computer vision and artificial intelligence. His research group has contributed competitive submissions to dense open-set recognition benchmarks and won several challenges in semantic segmentation of road-driving scenes.
Applications of generative approaches for artificial intelligence
Most machine learning applications leverage discriminative models and supervised learning. However, discriminative models are unable to generate new content or measure plausibility of the data. Additionally, they are prone to learning simple decision rules which perform well on standard benchmarks but fail in real-world scenarios. Generative approaches are a promising approach towards alleviating these problems due to ability to learn complementary information from the training dataset.
We are especially interested in a class of generative approaches that optimizes the data likelihood due to resistance to mode collapse. This presentation shall introduce prominent generative approaches and provide an overview of application fields in computer vision and natural language processing.