meta continual learning 另一个有吸引力的想法是用于continual learning的meta learning(这玩意儿真是万金水油,因为普适性,啥领域都能插一脚,这也导致一大堆meta learning和其它领域交叉的垃圾papers的诞生),它试图针对各种场景自动学习数据驱动的归纳偏差,而不是手动设计 [148]。 OML[185] 提供了一种元训练策略,对...
including foundation models, domain adaptation, meta-learning, test-time adaptation, generative models, reinforcement learning and federated learning. 通过这样做,我们对遗忘进行了全面的检查,涵盖了更广泛的背景和应用。 在本次调查中,我们根据具体的应用场景,将机器学习中的遗忘分为两类:有害遗忘和有益遗忘。
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-...
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learning aims to alleviate this issue by learning effectivel... P Bateni,J Barber,R Goyal,... ...
这篇论文我都不想读完,论文撰写没什么问题,但是论文核心思想太浅了,没东西,有效性证明的很片面,严格点可以直接质疑论文观点正确性。 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...
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, ...
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 ...
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...
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 ...
In this work, we replace the standard neuron by a 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 ...