Class-Incremental Mixture of Gaussians for Deep Continual Learning
Lukasz Korycki Bartosz Krawczyk
Continual learning models for stationary data focus on learning and retaining
concepts coming to them in a sequential manner. In the most generic
class-incremental environment, we have to be ready to deal with classes coming
one by one, without any higher-level grouping. This requirement invalidates
many previously proposed methods and forces researchers to look for more
flexible alternative approaches. In this work, we follow the idea of
centroid-driven methods and propose end-to-end incorporation of the mixture of
Gaussians model into the continual learning framework. By employing the
gradient-based approach and designing losses capable of learning discriminative
features while avoiding degenerate solutions, we successfully combine the
mixture model with a deep feature extractor allowing for joint optimization and
adjustments in the latent space. Additionally, we show that our model can
effectively learn in memory-free scenarios with fixed extractors. In the
conducted experiments, we empirically demonstrate the effectiveness of the
proposed solutions and exhibit the competitiveness of our model when compared
with state-of-the-art continual learning baselines evaluated in the context of
image classification problems.