Class-incremental learning(CIL)已经在从少量类别(基础类别)开始的情况下得到了广泛的研究。相反,我们探讨了一个在现实世界中鲜有研究的CIL设置,该设置从已经在大量基础类别上进行了预训练的强大模型开始。我们假设一个强大的基础模型可以为新颖的类别提供良好的表示,并且可以通过小的调整来进行增量学习。我们提出了一个...
与其他CIL方法相比,MEMO具有以下优点: 1. 内存效率:MEMO使用注意力机制来选择最相关的示例,从而减少了存储示例所需的内存。 2. 知识蒸馏效果:MEMO使用知识蒸馏来保持旧类别的知识,并且可以在不影响内存预算的情况下提高性能。 3. 适用性:MEMO适用于各种不同类型的支撑骨干和数据集,并且可以在不同内存预算下进行优化。
In class-incremental learning (CIL), we first focus on one incremental step, and generalize into multiple steps in Sec- tion 3.3. Given a base model Mb pre-trained on a label set Yb using the base dataset Db, we augment Yb with another label set Yn using dataset Dn,...
Topology-Preserving Class-Incremental Learning (TPCIL)Class-Incremental Learning (CIL)Elastic Hebbian Graph (EHG)Topology-Preserving Loss (TPL)A well-known issue for class-incremental learning is the catastrophic forgetting phenomenon, where the network's recognition performance on old classes degrades ...
Few-Shot Class-Incremental Learning (FSCIL)presents an extension of the Class Incremental Learning (CIL)problem where a model is faced with the problem of data scarcity while addressing the Catastrophic Forgetting (CF)problem. This problem remains an open problem because all recent works are built...
CVPR2020 论文地址: https://arxiv.org/pdf/2004.10956.pdf CVPR2020 本篇,FSCIL,西交大提出的。将NG网络运用到增量学习之中。 ECCV2020,TPCIL,也是西交大的同一个人发的,Topology Preserving Class-Incremental learning,同样的框架,即CNN+拓扑结构,部分内容换了一个写法。 CVPR... ...
and an opinion monitoring system should analyze emerging topics every day. Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally and build a universal classifier among all seen classes. Correspondingly, when directly training the model with new cl...
Class-Incremental Learning (CIL). duh. Catastrophic Interference/Forgetting. Catastrophic interference, also known as catastrophic forgetting, is the tendency of anartificial neural networkto completely and abruptly forget previously learned information upon learning new information. ...
Distilling Causal Effect of Data in Class-Incremental Learningarxiv.org/pdf/2103.01737.pdf 作者想要借助因果关系解释CIL中的遗忘(forgeting)与反遗忘(anti-forgeting) Definition D:old data, I:new data中的训练样本, ,X,Xo :当前model提取的feature和旧model提取的feature, ,Y,Yo :当前model预测的label...
具体来说,在LT-CIL中,对于任务 t ,首先使用 \mathcal{L}_{\mathrm{CE},t} 和\mathcal{L}_{\mathrm{aux},t} 来从第一阶段学习一个模型(如 Fig.2 顶部所示)。在第二阶段,附加了一个单个可训练层,称之为可学习权重缩放(LWS)层 \mathbf{W}\in\mathbb{R}^{C_{1:t}\times1} ,其维度等于任务...