This paper presents a class incremental learning (IL) method which exploits fine tuning and a dual memory to reduce the negative effect of catastrophic forgetting in image recognition. First, we simplify the current fine tuning based approaches which use a combination of classification and ...
Due to the fixed size of the covariance matrix, this method has low memory consumption. Both methods achieve good results in FSCIL, but the drawback is that the modeling process is complex. 4.1.3 Function optimization Existing methods focus on overcoming catastrophic forgetting when learning new ...
该论文的题目为《Class-Incremental Learning via Dual Augmentation 》,是中国科学院大学的一篇文章,发表于neural information processing systems(NeurIPS),Dec 2021。原文地址:proceedings.neurips.cc/。 该文章提出的持续学习方法为(non-exemplar based Class-IL)类增量学习方法(新的任务与旧的任务具有不同的类别),模...
Il2m: Class incremen- tal learning with dual memory. In International Conference on Computer Vision (ICCV), 2019. [4] Eden Belouadah and Adrian Popescu. Scail: Classifier weights scaling for class incremental learning. In Winter C...
Class-Incremental Learning via Dual Augmentation. NeurIPS'21 作者:Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu [Incremental Learning] & [Transfer Learning] 这篇文章关注增量学习中的类增量学习问题。类增量学习关注对T个任务的连续学习,这T个任务的数据类别互斥。在第t个阶段,模型需要学习来自第t个...
RMM: RMM: Reinforced Memory Management for Class-Incremental Learning. NeurIPS2021 [paper] IL2A: Class-Incremental Learning via Dual Augmentation. NeurIPS2021 [paper] ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection. NeurIPS 2022 [paper] SSRE: Self-Sustaining ...
Within the realm of incremental learning, prompt-based methods such as DualPrompt [35] and L2P [36] capture both task-invariant and task-specific knowledge but still face challenges in few-shot class-incremental learning. For Few-Shot Class-Incremental Learning (FSCIL), recent multimodal prompt...
Zhou, D. W., Wang, Q. W., Ye, H. J. & Zhan, D. C. (2023). A model or 603 exemplars: Towards memory-efficient class-incremental learning.ICLR. Zhou, D. W., Ye, H. J., Zhan, D. C. & Liu, Z. (2024). Revisiting class-incremental learning with pre-trained models: Genera...
3. Method Our goal is to enable the network to learn multiple tasks se- quentially with imbalanced data streams. In this section, we present the problem definition of long-tail class incremental learning. After that, we detail the proposed method. 3.1. Pr...
Paper tables with annotated results for DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning