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In many real-life tasks of application of supervised learning approaches, allthe training data are not available at the same time. The examples are lifelongimage classification or recognition of environmental objects during interactionof instrumented persons with their environment, enrichment of anonline-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 isthe task of incremental/continual learning approaches. Amongst differentproblems to be solved by these approaches such as introduction of newcategories in the model, refining existing categories to sub-categories andextending trained classifiers over them, ... we focus on the problem ofadjusting pre-trained model with new additional training data for existingcategories. We propose a fast continual learning layer at the end of theneuronal network. Obtained results are illustrated on the opensource CIFARbenchmark dataset. The proposed scheme yields similar performances asretraining but with drastically lower computational cost.

Move-to-Data: A new Continual Learning approach with Deep CNNs, Application for image-class recognition