FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
NeurIPS(2023)
摘要
Exemplar-free class-incremental learning (CIL) poses several challenges since
it prohibits the rehearsal of data from previous tasks and thus suffers from
catastrophic forgetting. Recent approaches to incrementally learning the
classifier by freezing the feature extractor after the first task have gained
much attention. In this paper, we explore prototypical networks for CIL, which
generate new class prototypes using the frozen feature extractor and classify
the features based on the Euclidean distance to the prototypes. In an analysis
of the feature distributions of classes, we show that classification based on
Euclidean metrics is successful for jointly trained features. However, when
learning from non-stationary data, we observe that the Euclidean metric is
suboptimal and that feature distributions are heterogeneous. To address this
challenge, we revisit the anisotropic Mahalanobis distance for CIL. In
addition, we empirically show that modeling the feature covariance relations is
better than previous attempts at sampling features from normal distributions
and training a linear classifier. Unlike existing methods, our approach
generalizes to both many- and few-shot CIL settings, as well as to
domain-incremental settings. Interestingly, without updating the backbone
network, our method obtains state-of-the-art results on several standard
continual learning benchmarks. Code is available at
https://github.com/dipamgoswami/FeCAM.
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关键词
class distributions,learning,fecam,exemplar-free
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