# 基于最优传输的在线增量学习潜在空间动态构建方法

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-16 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo2z0qjp04p8slbapn83gvfn
- 原文链接：https://arxiv.org/abs/2211.16780

## AI 摘要

针对在线增量学习中数据分布持续偏移、旧样本重放价值有限的挑战，研究者提出基于最优传输理论的在线混合模型学习框架（MMOT）。该方法摒弃传统单一或多固定类质心表示，使质心随新数据流增量演化，从而更精确刻画多模态复杂数据分布，并提升对未见样本的类相似度估计精度。同时，动态保持策略通过调节潜在空间维持类间可分离性，有效缓解灾难性遗忘。实验验证表明，该方法在基准数据集上具有显著优势。

## 正文

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.