Muhammad Umer

Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks

2020

Artificial neural networks are well-known to be susceptible to catastrophic forgetting when continually learning from sequences of tasks. Various continual (or "incremental") learning approaches have been proposed to avoid catastrophic forgetting, but they are typically …

Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models

2021

Continual (or “incremental”) learning approaches are employed when additional knowledge or tasks need to be learned from subsequent batches or from streaming data. However these approaches are typically adversary agnostic, i.e., they do not consider …