… a network that can continually accumulate knowledge over different tasks without the need
… to alleviate forgetting. We focus on task-incremental classification, where tasks arrive in a …

… We introduce another category to the existing taxonomy of CL, the emerging concept of …
while minimizing forgetting and preserving client privacy is a challenge unique to distributed …

… for the task in the network can be applied to classify the test … is class continual learning,
which does not provide the task id … Task-CL setting, we don’t want the training of the new task to …

… possible reasons to catastrophic forgetting on BERT. (1) The … Hence, we can use the mean
classification accuracy as a … during continual learning, where T is the number of learned tasks, …

… The major challenge in incremental learning is the so-called … top-1 classification accuracy.
In addition, we report the … forgetting across tasks and datasets. Through additional component …

… learning field, continual learning has gained more attention since the catastrophic forgetting
problem poses a critical challenge for … forgetting, we provide a comparison of the first task’s …

… on the road for continual learning systems, which are pre… set of methods have been presented
for overcoming this challenge. … do not well represent modern classification tasks. The two-…

… This paper proposes a novel TCL model called CAT (Continual learning with forgetting …
3For example, one task is to classify fish and non-fish, and another task is to classify different …

… for the Continual Learning (CL) problem for classification—a learning task where a stream
… , they are prone to the phenomenon called catastrophic forgetting [1, 2]. Significant attention …

… continual learning by allowing a model to continuously learn on temporal data. To solve the
… forgetting problem of learning temporal data in task incremental scenarios, in this research, …