Search Results for author:
Found 4 papers, 0 papers with code
Lifelong Learning Without a Task Oracle
Supervised deep neural networks are known to undergo a sharp decline in the accuracy of older tasks when new tasks are learned, termed "catastrophic forgetting".
Beneficial Perturbations Network for Defending Adversarial Examples
The reason is that neural networks fail to accommodate the distribution drift of the input data caused by adversarial perturbations.
Beneficial Perturbation Network for designing general adaptive artificial intelligence systems
Continual learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby a previously learned mapping of an old task is erased when learning new mappings for new tasks.
Closed-Loop Memory GAN for Continual Learning
Sequential learning of tasks using gradient descent leads to an unremitting decline in the accuracy of tasks for which training data is no longer available, termed catastrophic forgetting.
