  In order to address real-world problems, deep learning models are jointly
trained on many classes. However, in the future, some classes may become
restricted due to privacy/ethical concerns, and the restricted class knowledge
has to be removed from the models that have been trained on them. The available
data may also be limited due to privacy/ethical concerns, and re-training the
model will not be possible. We propose a novel approach to address this problem
without affecting the model's prediction power for the remaining classes. Our
approach identifies the model parameters that are highly relevant to the
restricted classes and removes the knowledge regarding the restricted classes
from them using the limited available training data. Our approach is
significantly faster and performs similar to the model re-trained on the
complete data of the remaining classes.
