  In order to adhere to regulatory standards governing individual data privacy
and safety, machine learning models must systematically eliminate information
derived from specific subsets of a user's training data that can no longer be
utilized. The emerging discipline of Machine Unlearning has arisen as a pivotal
area of research, facilitating the process of selectively discarding
information designated to specific sets or classes of data from a pre-trained
model, thereby eliminating the necessity for extensive retraining from scratch.
The principal aim of this study is to formulate a methodology tailored for the
purposeful elimination of information linked to a specific class of data from a
pre-trained classification network. This intentional removal is crafted to
degrade the model's performance specifically concerning the unlearned data
class while concurrently minimizing any detrimental impacts on the model's
performance in other classes. To achieve this goal, we frame the class
unlearning problem from a Bayesian perspective, which yields a loss function
that minimizes the log-likelihood associated with the unlearned data with a
stability regularization in parameter space. This stability regularization
incorporates Mohalanobis distance with respect to the Fisher Information matrix
and $l_2$ distance from the pre-trained model parameters. Our novel approach,
termed \textbf{Partially-Blinded Unlearning (PBU)}, surpasses existing
state-of-the-art class unlearning methods, demonstrating superior
effectiveness. Notably, PBU achieves this efficacy without requiring awareness
of the entire training dataset but only to the unlearned data points, marking a
distinctive feature of its performance.
