When Meta-Learning Meets Online and Continual Learning: A Survey

• URL: http://arxiv.org/abs/2311.05241v3
• Date: Fri, 08 Nov 2024 02:36:57 GMT
• Title: When Meta-Learning Meets Online and Continual Learning: A Survey
• Authors: Jaehyeon Son, Soochan Lee, Gunhee Kim,
• Abstract summary: meta-learning is a data-driven approach to optimize the learning algorithm. Continual learning and online learning, both of which involve incrementally updating a model with streaming data. This paper organizes various problem settings using consistent terminology and formal descriptions.
• Score: 39.53836535326121
• License:
• Abstract: Over the past decade, deep neural networks have demonstrated significant success using the training scheme that involves mini-batch stochastic gradient descent on extensive datasets. Expanding upon this accomplishment, there has been a surge in research exploring the application of neural networks in other learning scenarios. One notable framework that has garnered significant attention is meta-learning. Often described as "learning to learn," meta-learning is a data-driven approach to optimize the learning algorithm. Other branches of interest are continual learning and online learning, both of which involve incrementally updating a model with streaming data. While these frameworks were initially developed independently, recent works have started investigating their combinations, proposing novel problem settings and learning algorithms. However, due to the elevated complexity and lack of unified terminology, discerning differences between the learning frameworks can be challenging even for experienced researchers. To facilitate a clear understanding, this paper provides a comprehensive survey that organizes various problem settings using consistent terminology and formal descriptions. By offering an overview of these learning paradigms, our work aims to foster further advancements in this promising area of research.

Related papers

• A Unified Framework for Neural Computation and Learning Over Time [56.44910327178975]
  Hamiltonian Learning is a novel unified framework for learning with neural networks "over time" It is based on differential equations that: (i) can be integrated without the need of external software solvers; (ii) generalize the well-established notion of gradient-based learning in feed-forward and recurrent networks; (iii) open to novel perspectives.
  arXiv  Detail & Related papers  (2024-09-18T14:57:13Z)
• Concept Discovery for Fast Adapatation [42.81705659613234]
  We introduce concept discovery to the few-shot learning problem, where we achieve more effective adaptation by meta-learning the structure among the data features. Our proposed method Concept-Based Model-Agnostic Meta-Learning (COMAML) has been shown to achieve consistent improvements in the structured data for both synthesized datasets and real-world datasets.
  arXiv  Detail & Related papers  (2023-01-19T02:33:58Z)
• Hierarchically Structured Task-Agnostic Continual Learning [0.0]
  We take a task-agnostic view of continual learning and develop a hierarchical information-theoretic optimality principle. We propose a neural network layer, called the Mixture-of-Variational-Experts layer, that alleviates forgetting by creating a set of information processing paths. Our approach can operate in a task-agnostic way, i.e., it does not require task-specific knowledge, as is the case with many existing continual learning algorithms.
  arXiv  Detail & Related papers  (2022-11-14T19:53:15Z)
• Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems [46.760568562468606]
  Few-shot meta-learning optimize learning algorithms that can efficiently adapt to new tasks quickly. This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
  arXiv  Detail & Related papers  (2022-10-03T17:15:36Z)
• On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting [51.41426141283203]
  User-defined keyword spotting is a task to detect new spoken terms defined by users. Previous works try to incorporate self-supervised learning models or apply meta-learning algorithms. Our result shows that HuBERT combined with Matching network achieves the best result.
  arXiv  Detail & Related papers  (2022-04-01T10:59:39Z)
• Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review [4.462334751640166]
  This article reviews research studies that combine (two of) these learning algorithms. Based on the knowledge accumulated from the literature, we hypothesize a generic task-agnostic and model-agnostic learning network.
  arXiv  Detail & Related papers  (2021-11-23T20:41:06Z)
• Online Structured Meta-learning [137.48138166279313]
  Current online meta-learning algorithms are limited to learn a globally-shared meta-learner. We propose an online structured meta-learning (OSML) framework to overcome this limitation. Experiments on three datasets demonstrate the effectiveness and interpretability of our proposed framework.
  arXiv  Detail & Related papers  (2020-10-22T09:10:31Z)
• Meta-Learning in Neural Networks: A Survey [4.588028371034406]
  This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods.
  arXiv  Detail & Related papers  (2020-04-11T16:34:24Z)
• Provable Meta-Learning of Linear Representations [114.656572506859]
  We provide fast, sample-efficient algorithms to address the dual challenges of learning a common set of features from multiple, related tasks, and transferring this knowledge to new, unseen tasks. We also provide information-theoretic lower bounds on the sample complexity of learning these linear features.
  arXiv  Detail & Related papers  (2020-02-26T18:21:34Z)
• Revisiting Meta-Learning as Supervised Learning [69.2067288158133]
  We aim to provide a principled, unifying framework by revisiting and strengthening the connection between meta-learning and traditional supervised learning. By treating pairs of task-specific data sets and target models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning. This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning.
  arXiv  Detail & Related papers  (2020-02-03T06:13:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.