In the proposed meta-training scheme, the update predictor is trained to minimize loss on a combination of current and past tasks. We show experimentally that the proposed approach works in the continual learning setting. 展开 关键词: Computer Science - Machine Learning ...
这篇工作听起来是meta-learning实际上就是用了用MAML,也没原创什么东西, 就是把模型每层中间加了个attention层, 把不同task训练得到的output layer收集起来来构造一个任务无关场景下的模型。任务无关就是测试中可能会出现任意一个训练过程中出现的任务所涉及到的类别(分类任务),因此需要对所有训练任务的类别进行保留...
Meta-Continual Learning Via Dynamic ProgrammingKrishnan RaghavanPrasanna Balaprakash
这个地方,vit的的self attention layers 中,并不是所有的layer都被特殊设计用于增量,按照作者的源代码,比如一共六层的self attention layer可能只有3个self attention layer被specificly designed for continual learning,其它的则和正常的self attention没有太大区别(细微的一些区别也和本文内容无关算是作者个人的设计风格...
Continual Learning 持续学习包括在保留之前的知识基础的同时,逐步训练一个新的模型,这在近年来引起了学界极大兴趣。通常情况下, 持续学习有两种设置:(1)任务持续学习,通过具有明确领域边界的新任务扩展知识;(2)类持续学习,在从同一数据集中分离出来的不同类别集上积累知识。在这项工作中,我们主要关注于任务的持续学...
1、Meta-Learning Representations for Continual Learning Khurram Javed, Martha White Department of Computing Science University of Alberta T6G 1P8 kjavedualberta.ca, whitemualberta.ca Abstract A continual learning agent should be able to build on top of existing knowledge to learn on new data ...
Meta-learning code has been taken and modified from :https://github.com/dragen1860/MAML-Pytorch For EWC, MER, and ER-Reservoir experiments, we modify the following implementation to be able to load our models :https://github.com/mattriemer/MER...
Global Tiered-ImageNet Rank (Simple CNAPS): Transductive CNAPS extends the Simple CNAPS framework to the transductive few-shot learning setting where all query examples are provided at once. This method uses a two-step transductive task-encoder for adapting the feature extractor as well as a soft...
and its learning capabilities generalize to any domain. Unlike previous continual learning methods, our method does not make any assumption about how tasks are constructed, delivered and how they relate to each other: it simply absorbs and retains training samples one by one, whether the stream of...
The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks. While meta-learning shows great potential for reducing interference between old and new tasks, the current training procedures tend to be either ...