  The intrinsic difficulty in adapting deep learning models to non-stationary
environments limits the applicability of neural networks to real-world tasks.
This issue is critical in practical supervised learning settings, such as the
ones in which a pre-trained model computes projections toward a latent space
where different task predictors are sequentially learned over time. As a matter
of fact, incrementally fine-tuning the whole model to better adapt to new tasks
usually results in catastrophic forgetting, with decreasing performance over
the past experiences and losing valuable knowledge from the pre-training stage.
In this paper, we propose a novel strategy to make the fine-tuning procedure
more effective, by avoiding to update the pre-trained part of the network and
learning not only the usual classification head, but also a set of
newly-introduced learnable parameters that are responsible for transforming the
input data. This process allows the network to effectively leverage the
pre-training knowledge and find a good trade-off between plasticity and
stability with modest computational efforts, thus especially suitable for
on-the-edge settings. Our experiments on four image classification problems in
a continual learning setting confirm the quality of the proposed approach when
compared to several fine-tuning procedures and to popular continual learning
methods.
