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New categories may be introduced over time, or existing categories may needto be reclassified. Class incremental learning (CIL) is employed for thegradual acquisition of knowledge about new categories while preservinginformation about previously learned ones in such dynamic environments. Itmight also be necessary to also eliminate the influence of related categorieson the model to adapt to reclassification. We thus introduce class-levelmachine unlearning (MU) within CIL. Typically, MU methods tend to betime-consuming and can potentially harm the model's performance. A continuousstream of unlearning requests could lead to catastrophic forgetting. To addressthese issues, we propose a non-destructive eCIL-MU framework based on embeddingtechniques to map data into vectors and then be stored in vector databases. Ourapproach exploits the overlap between CIL and MU tasks for acceleration.Experiments demonstrate the capability of achieving unlearning effectivenessand orders of magnitude (upto $\sim 278\times$) of acceleration.

eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning