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Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.
"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinualAI.org. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks".