Unsupervised domain adaptation (UDA)enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs from that of the target. However, UDA is not always successful and several accounts of'negative transfer' have been reported in the literature.

How the regret scales in a finite time horizonT with problem parameters is a central theme in studies of different algorithms.

These models can be easily customized for new concepts using low-rank adaptations (LoRAs).

We present an approach for lifelong/continual learning of convolutional neural networks (CNN) that does not suffer from the problem of catastrophic forgetting when moving from onetask totheother. Weshowthat theactivation maps generated by the CNN trained on the old task can be calibrated using very few calibration parameters, to become relevant to the new task.