Incremental Learning of Visual Categories
نویسنده
چکیده
Incremental learning of visual categories denotes the capability of a visual perceptual system to build up an increasing repertoire of visual concepts based on a sequence of experiences. A visual category is here defined as a possibly large group of individual objects that share similar properties like shape, appearance, or color. Biological visual systems achieve this function very efficiently for their behaviorally relevant categories, where an appropriate generation and selection of features is considered to be responsible for good generalization. Static visual learning models with a fixed a priori set of trainable parameters face severe problems for dynamically changing training sets. In contrast to non-incremental visual approaches incremental ones approach this problem by growing categorical representations that adapt to successively available training stimuli.
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