Online Continual Learning Via Candidates Voting
release_gj5x52v74fe5tijmi4r4zsws3q
by
Jiangpeng He, Fengqing Zhu
2021
Abstract
Continual learning in online scenario aims to learn a sequence of new tasks
from data stream using each data only once for training, which is more
realistic than in offline mode assuming data from new task are all available.
However, this problem is still under-explored for the challenging
class-incremental setting in which the model classifies all classes seen so far
during inference. Particularly, performance struggles with increased number of
tasks or additional classes to learn for each task. In addition, most existing
methods require storing original data as exemplars for knowledge replay, which
may not be feasible for certain applications with limited memory budget or
privacy concerns. In this work, we introduce an effective and memory-efficient
method for online continual learning under class-incremental setting through
candidates selection from each learned task together with prior incorporation
using stored feature embeddings instead of original data as exemplars. Our
proposed method implemented for image classification task achieves the best
results under different benchmark datasets for online continual learning
including CIFAR-10, CIFAR-100 and CORE-50 while requiring much less memory
resource compared with existing works.
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