Meta-learningDeep learning has accomplished impressive excellence in many fields. However, its achievement relies on a vast amount of marker data and when there is insufficient labeled data, the phenomenon of over-fitting will occur. On the other hand, the real world tends to be so non-...
Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction [PDF2] [Copy] [Kimi14] Authors: Yichen Wu ; Long-Kai Huang ; Renzhen Wang ; Deyu Meng ; Yi…
Continual meta learning - Meta continual learningTowards Continual Reinforcement Learning: A Review and Perspectives, by Khetarpal et al, arXiv, 2020. Continual Unsupervised Representation Learning, by D. Rao et al., NeurIPS 2019. Distributed Continual Learning Ex-Model: Continual Learning from...
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...
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 ...
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 ...
Lastly, meta-learning for continual learning (see ‘Meta-Learning: Discovering Inductive Biases for Continual Learning’) is an approach that is motivated by the brain’s ability to synthesize novel solutions after limited experience [8]. Through applying machine learning to optimize the learning appr...
问题定义：持续学习（Continual Learning, CL）中的灾难性遗忘问题。在传统监督学习中，模型在独立同分布（i.i.d）样本上训练。然而，在持续学习中，模型需在非静态数据分布上学习，导致在新任务学习时遗忘之前任务知识。这篇论文提出Variance Reduced Meta-CL (VR-MCL)方法，结合正则化方法与元持续学习...
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 ...
meta-learned neuron model whom inference and update rules are optimized to minimize catastrophic interference. Our approach can memorize dataset-length sequences of training samples, and its learning capabilities generalize to any domain. Unlike previous continual learning methods, our method does not ...