Class-Incremental Mixture of Gaussians for Deep Continual Learning

Lukasz Korycki, Bartosz Krawczyk; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4097-4106

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.