Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation

1 code implementation • 7 Mar 2024 • Jiyong Li, Dilshod Azizov, Yang Li, Shangsong Liang

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings.

Continual Learning Contrastive Learning +2

Computational Complexity of Sub-Linear Convergent Algorithms

no code implementations • 29 Sep 2022 • Hilal AlQuabeh, Farha AlBreiki, Dilshod Azizov

One of these approaches is reducing the gradient variance through adaptive sampling to solve large-scale optimization's empirical risk minimization (ERM) problems.