eCIL-MU: Embedding based Class Incremental Learning and Machine
  Unlearning
Zhiwei Zuo Zhuo Tang Bin Wang Kenli Li Anwitaman Datta
New categories may be introduced over time, or existing categories may need
to be reclassified. Class incremental learning (CIL) is employed for the
gradual acquisition of knowledge about new categories while preserving
information about previously learned ones in such dynamic environments. It
might also be necessary to also eliminate the influence of related categories
on the model to adapt to reclassification. We thus introduce class-level
machine unlearning (MU) within CIL. Typically, MU methods tend to be
time-consuming and can potentially harm the model's performance. A continuous
stream of unlearning requests could lead to catastrophic forgetting. To address
these issues, we propose a non-destructive eCIL-MU framework based on embedding
techniques to map data into vectors and then be stored in vector databases. Our
approach exploits the overlap between CIL and MU tasks for acceleration.
Experiments demonstrate the capability of achieving unlearning effectiveness
and orders of magnitude (upto $\sim 278\times$) of acceleration.