Inspired Approach for Interactive Learning of Categories

An amazing capability of the human visual system is the ability to learn a large repertoire of visual categories. We propose an architecture for learning visual categories in an interactive and life-long fashion based on complex-shaped objects, which typically belong to several different categories. The fundamental problem of life-long learning with artificial neural networks is the so-called "stability-plast icity dilemma". This dilemma refers to the incremental incorporation of newly acquired knowledge, while also the earlier learned informa­ tion should be preserved. To achieve this learning ability we propose biologically inspired modifications to the established learning vector quantization (LVQ) approach and combine it with a category-specific forward feature selection to decouple co-occurring categories. Both parts are optimized together to ensure a compact and efficient category representation, which is necessary for fast and interactive learning.