We show that the influence of a subset of the training samples can be removed--or"
forgotten"--from the weights of a network trained on large-scale image classification tasks …

We explore the problem of selectively forgetting a particular subset of the data used for
training a deep neural network. While the effects of the data to be forgotten can be hidden …

Abstract We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large
models on a diverse collection of data while minimizing the expected cost to remove the …

A catastrophic forgetting problem makes deep neural networks forget the previously learned
information, when learning data collected in new environments, such as by different sensors …

This paper presents a method for adding multiple tasks to a single deep neural network
while avoiding catastrophic forgetting. Inspired by network pruning techniques, we exploit …

Forgetting refers to the loss or deterioration of previously acquired information or knowledge.
While the existing surveys on forgetting have primarily focused on continual learning …

Lifelong learning with deep neural networks is well-known to suffer from catastrophic
forgetting: the performance on previous tasks drastically degrades when learning a new …

The practical needs of the" right to be forgotten" and poisoned data removal call for efficient
machine unlearning techniques, which enable machine learning models to unlearn, or to …

Life-long learning aims at learning a sequence of tasks without forgetting the previously
acquired knowledge. However, the involved training data may not be life-long legitimate due …

We describe a procedure for removing dependency on a cohort of training data from a
trained deep network that improves upon and generalizes previous methods to different …