  Machine Unlearning is an emerging paradigm for selectively removing the
impact of training datapoints from a network. Unlike existing methods that
target a limited subset or a single class, our framework unlearns all classes
in a single round. We achieve this by modulating the network's components using
memory matrices, enabling the network to demonstrate selective unlearning
behavior for any class after training. By discovering weights that are specific
to each class, our approach also recovers a representation of the classes which
is explainable by design. We test the proposed framework on small- and
medium-scale image classification datasets, with both convolution- and
Transformer-based backbones, showcasing the potential for explainable solutions
through unlearning.
