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网络能够很好学习和保存不同类形成的特征拓扑结构. 本文提出了TOpology-Preserving knowledge InCrementer(TOPIC) 框架. TOPIC通过稳定NG...
[15] Mengye Ren, Renjie Liao, Ethan Fetaya, and Richard S. Zemel. Incremental few-shot learning with attention attractor networks. In NeurIPS, 2019. [16] Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, Xinyuan Chang, and Xing Wei. Few-shot class-incremental learning via relation knowledge distillati...
On the soft-subnetwork for few-shot class incremental learning Warping the space: Weight space rotation for class-incremental few-shot learning Neural collapse inspired feature-classifier alignment for few-shot class-incremental learning Learning with fantasy: Semantic-aware virtual contrastive constraint fo...
[arxiv 20200628] Few-Shot Class-Incremental Learning via Feature Space Composition :Few-ShotClass-IncrementalLearning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)D^{(t)}D(t),t>;1t>;1t>;1,且数据集中的类别与基础数据...
Few-Shot Class-Incremental Learning via Cross-Modal Alignment with Feature Replay Few-shot class-incremental learning (FSCIL) studies the problem of continually learning novel concepts from a limited training data without catastrophicall... Y Li,L He,F Lin,... - Chinese Conference on Pattern Rec...
简介: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 ...
few-shot class-incremental learning Few-shot class-incremental learning is a form of machine learning that focuses on the ability to teach a model to generalize from a limited number of examples and then continually and incrementally adapt to new classesof data without catastrophic forgetting. This...
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。
Few-Shot Class-Incremental Learning for Named Entity Recognition论文阅读笔记 Abstract 之前的面向NER的类增量学习的工作都是基于新类有丰富的监督数据的情况,本文聚焦更具挑战且更实用的问题:少样本NER的增量学习。模型只用少量新类样本进行训练,保证新类效果的前提下不遗忘旧类知识。为了解决少样本类增量学习的灾难...
Class-Incremental Learning: 类增量学习,旨在与所有人共同预测在不知道标签的情况下遇到的类。 Few-Shot Learning:少样本学习,使用基础数据集进行训练,然后用少量样本预测未知的目标类别。 Data-Free Distillation:Data-Free Distillation 是指在没有教师的训练数据的情况下,从教师模型蒸馏到学生模型的情况。一个典型的...