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 ...
Meta-Continual Learning Via Dynamic ProgrammingKrishnan RaghavanPrasanna Balaprakash
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...
We propose a novel framework termed as Meta-Continual Learning with Knowledge Embedding to address the task of jointly sketch, cartoon, and caricature face recognition. In particular, we firstly present a deep relational network to capture and memorize the relation among different samples. Secondly, ...
首先呢,假设一个self attention layer 被选中用于continual learning,则会额外初始化一个: ifself.TI_mask:## 这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里这里self.gamma=gammaself.num_patches=num_patches# token maskinit_n=int(math.sqrt...
In this paper, we implement the model-agnostic meta-learning (MAML) and Online aware Meta-learning (OML) meta-objective under the continual framework for NLU tasks. We validate our methods on selected SuperGLUE and GLUE benchmark. PDF Abstract ...
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 ...
Continual learning is the ability to acquire new knowledge without forgetting the previously learned one, assuming no further access to past training data. Neural network approximators trained with gradient descent are known to fail in this setting as they must learn from a stream of data-points ...
$ conda create -n yourenvname python=3.7 anaconda $ source activate yourenvname $ pip install -r requirements.txt $ git clone https://github.com/ntasfi/PyGame-Learning-Environment.git $ cd PyGame-Learning-Environment/ $ pip install -e . ...