Incremental Learning of Object Detectors without Catastrophic Forgetting

ICCV 2017 Konstantin ShmelkovCordelia SchmidKarteek Alahari

Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the initial training data. They suffer from "catastrophic forgetting" - an abrupt degradation of performance on the original set of classes, when the training objective is adapted to the new classes... (read more)

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