Class incremental learningFew-shot learningIn the scene of remote sensing image (RSI) recognition, it is difficult to obtain a sufficient number of samples for training all categories at once. A more realistic situation is that the recognition task occurs in an open environment, with categories ...
2020年，Tao等人首次提出了Few-Shot Class-Incremental Learning（FSCIL）的概念，旨在解决这些问题。FSCIL结合了少样本学习和增量学习的优点，使模型能够从少量样本中学习新类别，同时保持对之前学习内容的记忆。这一概念不仅仅是学术上的进步，更代表了一种更接近人类学习方式的机器学习范式。FSCIL的学习过程通常包含一...
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...
核心思想 本文提出一种用于解决小样本类别增量学习(few-shot class-incremental learning, FSCIL)的算法(TOPIC)。首先解释一下什么是小样本类别增量学习,模型首先在一个大规模的基础数据集D(1)D^{(1)}D(1)上进行训练,然后会不断增加新的数据集D(t),t>1D^{(t)}, t>1D(t),t>1,且数据... ...
M2SD: Multiple Mixing Self-Distillation for Few-Shot Class-Incremental Learning论文下载 论文作者 Jinhao Lin, Ziheng Wu, Weifeng Lin, Jun Huang, Rong Hua Luo 内容简介 本文提出了一种新颖的方法,称为多混合自蒸馏(M2SD),旨在解决少样本类增量学习(FSCIL)中的挑战。FSCIL的目标是在有限的实例中识别新...
Few-Shot Class-Incremental Learning读书笔记 xuuuu 9 人赞同了该文章小样本类增量学习要解决两个问题:灾难性遗忘和过拟合问题。 Few-Shot Class-Incremental Learning(FSCIL)需要CNN模块从非常少的标签样本增量地学习新的类别,同时不忘记之前的任务.为了解决这个问题,本文使用NG(neural gas) network来表示知识, NG...
本文是一篇关于少量样本增量学习(Few-shot Class-Incremental Learning, FSCIL)的综述,提出了一种新的分类方法,将FSCIL分为五个子类别,并提供了广泛的文献回顾和性能评估,讨论了FSCIL的定义、挑战、相关学习问题以及在计算机视觉领域的应用。 1 介绍 年份:2024 ...
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... ...
PAPER{CVPR' 2021}Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning URL论文地址 CODE代码地址 1.1 Motivation# · 小样本增量学习增量类别样本过少,不足以训练好分类和蒸馏过程,不能像现有增量学习方法那样促进表示空间进一步扩展。
18 Sep 2024·Cuiwei Liu,Siang Xu,Huaijun Qiu,Jing Zhang,Zhi Liu,Liang Zhao· Few-shot class-incremental learning is crucial for developing scalable and adaptive intelligent systems, as it enables models to acquire new classes with minimal annotated data while safeguarding the previously accumulated...