An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning

ArXi:2211.16780v3 Announce Type: replace 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