  This paper introduces a new lifelong learning solution where a single model
is trained for a sequence of tasks. The main challenge that vision systems face
in this context is catastrophic forgetting: as they tend to adapt to the most
recently seen task, they lose performance on the tasks that were learned
previously. Our method aims at preserving the knowledge of the previous tasks
while learning a new one by using autoencoders. For each task, an
under-complete autoencoder is learned, capturing the features that are crucial
for its achievement. When a new task is presented to the system, we prevent the
reconstructions of the features with these autoencoders from changing, which
has the effect of preserving the information on which the previous tasks are
mainly relying. At the same time, the features are given space to adjust to the
most recent environment as only their projection into a low dimension
submanifold is controlled. The proposed system is evaluated on image
classification tasks and shows a reduction of forgetting over the
state-of-the-art
