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...
TPL方法的核心思想是利用CIL(Class Incremental Learning)中的额外信息,如重放数据和已学习的任务信息,来设计一个更优化的任务ID预测方法。该方法通过以下几个关键技术实现: 似然比估计:TPL通过比较一个测试样本属于当前任务的概率与属于其他任务的概率来预测任务ID。这个比较是通过计算似然比来实现的,即将样本属于当前任...
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,...
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 severely when incrementally learning new classes. To ...
In class incremental learning (CIL) a model must learn new classes in a sequential manner without forgetting old ones. However, conventional CIL methods consider a balanced distribution for each new task, which ignores the prevalence of long-tailed distributions in the real world. In this work ...
In the context of class- incremental learning (CIL), training a classification model is a sequential process where each step consists in integrat- ing a set of new classes to the model. This process is par- ticularly challenging in the exemplar-free setting (EFCIL), in which the mode...
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...
In the field of class incremental learning (CIL), generative replay has become increasingly prominent as a method to mitigate the catastrophic forgetting, alongside the continuous improvements in generative models. However, its application in class incremental object detection (CIOD) has been ...
技术标签:continual learning机器学习论文解析 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...
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...