Forward Compatible Few-Shot Class-Incremental Learning (CVPR 2022)速查笔记 暴戾无言 若非坚决至暴戾,亦可静默着抗争。5 人赞同了该文章 论文链接:2203.06953.pdf (arxiv.org) 代码链接:https: //github.com/zhoudw-zdw/CVPR22-Fact 摘要 在我们动态变化的世界中经常出现新的类别,例如认证系统中的新用户,...
FSCIL(Few-shot class-incremental learning)旨在设计机器学习算法,使其能够不断地从少数数据点中学习新的概念,而不忘记旧类的知识。困难在于,来自新类的有限数据不仅会导致严重的过拟合问题,还会加剧臭名昭著的灾难性遗忘问题。此外,由于训练数据在FSCIL中是按顺序出现的,学习到的分类器只能提供单个会话的判别信息,...
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。
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... ...
Few Shot Incremental Learning with Continually Evolved Classifiers CVPR2021,由新加坡南洋理工大学 本文利用Graph即图模型,将拓扑结构与增量模型向结合,从而取得不错的效果。 类似论文,均是基于双阶段的增量模型,一个是特征提取模块,另一个是分类器模块。 对于Rehearsal的方法而言,特征提取模块可能一起更... ...
In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. To address this problem, we represent the ...
Forward Compatible Few-Shot Class-Incremental Learning 论文/Paper:https://arxiv.org/abs/2203.06953 代码/Code:https://github.com/zhoudw-zdw/CVPR22-Fact XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding ...
Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting, and these challenges have driven ...
The implementation of CVPR 2023 paper Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning [paper]. If you use the code in this repo for your work, please cite the following bib entries: @inproceedings{song2023learning, title={Learning with ...
Few-shot Learning meta-learning transfer mechanism data generation 4★Setting 现有N个candidate base class,要求从中选择出M个base class。 在选择之前,每个candidate class包含少量(50张)标注样本。 当选择出M个base class之后,可以通过手工标注为这M个base class添加充足的标注样本,然后构造base dataset并训练base...