  In many real-life tasks of application of supervised learning approaches, all
the training data are not available at the same time. The examples are lifelong
image classification or recognition of environmental objects during interaction
of instrumented persons with their environment, enrichment of an
online-database with more images. It is necessary to pre-train the model at a
"training recording phase" and then adjust it to the new coming data. This is
the task of incremental/continual learning approaches. Amongst different
problems to be solved by these approaches such as introduction of new
categories in the model, refining existing categories to sub-categories and
extending trained classifiers over them, ... we focus on the problem of
adjusting pre-trained model with new additional training data for existing
categories. We propose a fast continual learning layer at the end of the
neuronal network. Obtained results are illustrated on the opensource CIFAR
benchmark dataset. The proposed scheme yields similar performances as
retraining but with drastically lower computational cost.
