  Humans can continuously learn new knowledge as their experience grows. In
contrast, previous learning in deep neural networks can quickly fade out when
they are trained on a new task. In this paper, we hypothesize this problem can
be avoided by learning a set of generalized parameters, that are neither
specific to old nor new tasks. In this pursuit, we introduce a novel
meta-learning approach that seeks to maintain an equilibrium between all the
encountered tasks. This is ensured by a new meta-update rule which avoids
catastrophic forgetting. In comparison to previous meta-learning techniques,
our approach is task-agnostic. When presented with a continuum of data, our
model automatically identifies the task and quickly adapts to it with just a
single update. We perform extensive experiments on five datasets in a
class-incremental setting, leading to significant improvements over the state
of the art methods (e.g., a 21.3% boost on CIFAR100 with 10 incremental tasks).
Specifically, on large-scale datasets that generally prove difficult cases for
incremental learning, our approach delivers absolute gains as high as 19.1% and
7.4% on ImageNet and MS-Celeb datasets, respectively.
