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...
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...
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...
To address that, we propose a novel two-stage FSCIL method using dual bridges containing sample bridge and memory bridge. In the base session, we train a model with the sample bridge, which is built by marriage of real samples, to preview knowledge. In incremental sessions, we align ...
CentraleSupe´lec, MICS, France {eva.feillet, gregoire.petit, adrian.popescu, marina.reyboz}@cea.fr, celine.hudelot@centralesupelec.fr Abstract Recent class-incremental learning methods combine deep neural architectures and learning algorithms to han- dle streaming data under memory and computat...
Paper tables with annotated results for DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning
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 ...
IL2M: Class Incremental Learning With Dual Memory This paper presents a class incremental learning (IL) method which exploits fine tuning and a dual memory to reduce the negative effect of catastrophic for... E Belouadah,A Popescu - 会议论文 被引量: 0发表: 2019年 Efficient Ultrasound Image ...
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...