Search Results for author:
Found 4 papers, 0 papers with code
Balancing Stability and Plasticity through Advanced Null Space in Continual Learning
Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i. e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well.
Online Continual Learning with Contrastive Vision Transformer
Based on the learnable focuses, we design a focal contrastive loss to rebalance contrastive learning between new and past classes and consolidate previously learned representations.
Continual Learning With Lifelong Vision Transformer
Continual learning methods aim at training a neural network from sequential data with streaming labels, relieving catastrophic forgetting.
Adaptive Curriculum Learning
Therefore, in contrast to recent works using a fixed curriculum, we devise a new curriculum learning method, Adaptive Curriculum Learning (Adaptive CL), adapting the difficulty of examples to the current state of the model.
