… Abstract—Continual learning paradigm learns from a continuous … adaptive progressive
network framework including two models for continual learning: Reinforced Continual Learning …

… Continual learning is key to advancing machine learning and artificial intelligence. Progressive
learning is a deep learning … of adaptive parameters (parameters in the progressive block …

… dynamics-adaptive continual reinforcement learning scheme (DaCoRL) with progressive …
are learned continually—in this case, continual learning can be referred to as incremental …

… Catastrophic forgetting is a key obstacle to continual learning. One of the state-of-the-art …
Gdumb: A simple approach that questions our progress in continual learning. In EECV, 524–…

… Continual learning There are numerous approaches in continual learning and we refer the
… The main approach of regularization-based methods in continual learning is to identify the …

… Approaches to continual learning aim to successfully learn a … enable deep learning to be
deployed in this learning scenario. A … be applied to other continual learning frameworks, see the …

The" pre-training-downstream adaptation" presents both new opportunities and challenges
for Continual Learning (CL). Although the recent state-of-the-art in CL is achieved through …

… learning paradigm by adapting the entire or partial model … a new continual learning method
called Learning to Prompt for … continual learning framework based on prompts for continual …

… , and are not capable of adapting to the needs of streaming data. Continual learning is a
subset of lifelong learning approaches that is aimed at learning such streaming data as they are …

… We believe that continual learning will be the key to the future machine’s intelligence. The …
CLeaR describes the prototype structure of the continual learning based framework. It can be …