Year: 2025
Type: conference-paper
Abstract: This study uncovers surprisingly effective synergies between the field of continual learning (CL) and machine unlearning (MUL).We extend the common class-incremental setting from CL to incorporate suppression requests in what we term class-incremental unlearning (CIUL).We present a light-weight approach to CIUL using replay/rehearsal-based CL approaches together with a selective replay strategy termed "Replay-To-Suppress" (RTS), where we actually make use of the catastrophic forgetting effect to achieve unlearning.In particular, we adapt a CL strategy termed adiabatic replay (AR) to achieve suppression at near-constant time complexity.We demonstrate excellent overall performance for all CL strategies extended by RTS on MNIST, F-MNIST and a latent encoded version of the challenging CIFAR and SVHN benchmarks.
Authors: Alexander Krawczyk, Alex Gepperth
Institution: Fulda University of Applied Sciences
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409478805