  Convolutional neural networks show remarkable results in classification but
struggle with learning new things on the fly. We present a novel rehearsal-free
approach, where a deep neural network is continually learning new unseen object
categories without saving any data of prior sequences. Our approach is called
RECALL, as the network recalls categories by calculating logits for old
categories before training new ones. These are then used during training to
avoid changing the old categories. For each new sequence, a new head is added
to accommodate the new categories. To mitigate forgetting, we present a
regularization strategy where we replace the classification with a regression.
Moreover, for the known categories, we propose a Mahalanobis loss that includes
the variances to account for the changing densities between known and unknown
categories. Finally, we present a novel dataset for continual learning,
especially suited for object recognition on a mobile robot (HOWS-CL-25),
including 150,795 synthetic images of 25 household object categories. Our
approach RECALL outperforms the current state of the art on CORe50 and
iCIFAR-100 and reaches the best performance on HOWS-CL-25.
