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...
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...
Few-shot incremental learning is the ability of a model to learn incrementally with limited data without forgetting.The challenge of incremental learning models is catastrophic forgetting.Prompt-guided knowledge distillation is used to minimize catastrophic forgetting.Attention-based knowledge distillation strat...
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 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...
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 (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of...
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... ...
1, a simple yet effective deep learning framework to tackle Few-Shot Class-Incremental Learning (FSCIL). FBNs are built upon the commonly used Deep Convolution Neural Networks (DCNNs) and support modern model architectures, e.g., VGG, Inception, and ResNet. In the forward pass, FBNs ...
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... ...