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This paper introduces a Mixture Model based on Optimal Transport (MMOT) for online incremental learning, addressing the challenge of distributional shifts and limited replay value of past samples. MMOT incrementally evolves centroids with new data, providing a more precise characterization of complex data streams and improving class similarity estimation. A Dynamic Preservation strategy is also introduced to regulate the latent space and mitigate catastrophic forgetting, leading to improved performance on benchmark datasets.
Forget fixed centroids: Optimal Transport lets you adaptively evolve class representations in latent space for better online incremental learning.
In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay value when learning a new task. Prior research has typically relied on either a single adaptive centroid or multiple fixed centroids to represent each class in the latent space. However, such methods struggle when class data streams are inherently multimodal and require continual centroid updates. To overcome this, we introduce an online Mixture Model learning framework grounded in Optimal Transport theory (MMOT), where centroids evolve incrementally with new data. This approach offers two main advantages: (i) it provides a more precise characterization of complex data streams, and (ii) it enables improved class similarity estimation for unseen samples during inference through MMOT-derived centroids. Furthermore, to strengthen representation learning and mitigate catastrophic forgetting, we design a Dynamic Preservation strategy that regulates the latent space and maintains class separability over time. Experimental evaluations on benchmark datasets confirm the superior effectiveness of our proposed method.