  While deep learning has achieved remarkable results on various applications,
it is usually data hungry and struggles to learn over non-stationary data
stream. To solve these two limits, the deep learning model should not only be
able to learn from a few of data, but also incrementally learn new concepts
from data stream over time without forgetting the previous knowledge. Limited
literature simultaneously address both problems. In this work, we propose a
novel approach, MetaCL, which enables neural networks to effectively learn meta
knowledge from low-shot data stream without catastrophic forgetting. MetaCL
trains a model to exploit the intrinsic feature of data (i.e. meta knowledge)
and dynamically penalize the important model parameters change to preserve
learned knowledge. In this way, the deep learning model can efficiently obtain
new knowledge from small volume of data and still keep high performance on
previous tasks. MetaCL is conceptually simple, easy to implement and
model-agnostic. We implement our method on three recent regularization-based
methods. Extensive experiments show that our approach leads to state-of-the-art
performance on image classification benchmarks.

Machine Learning;Machine Learning