… Our vision is for Auto-Adaptive ML systems which rely on continual learning. But just as
AutoML … architecture is classical learning methods as well as continual learning methods. The …

… There is enormous potential for the use of continual learning AI models in the practice of …
this powerful method are impressive and might ultimately change the practice of medicine. …

… datasets and dataset splits for continual learning, including the … for using replay-based
approaches to continual learning. … that questions our progress in continual learning. In European …

… continual learning framework named “DualNet", which comprises a fast learning system for
supervised learning of … learning into the slow learner, which is scalable in practice by training …

… to safely introduce image segmentation in clinical practice. One frontier to overcome when …
is the shift from static to continual learning. Continual learning, the practice of training models …

… general objectives of continual learning as ensuring … practice, training samples of different
distributions arrive in sequence. A continual learning model parameterized by θ needs to learn …

… continual learning in the medical domain, highlight recent trends, and point out the practical
issues. Specifically, we survey the continual learning … state of continual learning in medical …

… continual learning methods. Within this principled framework, we scrutinize 11 representative
approaches in the spectrum of continual learning … to be encountered in practice. Below, we …

… Continual Learning (VCL) framework achieves these desiderata on two benchmarks in continual
learning… The improvements are achieved by establishing a new best practice approach …

… We discuss a general formulation for the Continual Learning (CL) problem for classification—a
learning task where a stream provides samples to a learner and the goal of the learner, …