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<p>New knowledge originates from the old. The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models. In this survey, we comprehensively review and summarize the topic–“Historical Learning: Learning Models with Learning History”, which learns better neural models with the help of their learning history during its optimization, from three detailed aspects: Historical Type (what), Utility Part (where) and Storage Form (how). To our best knowledge, it is the first survey that systematically studies the methodologies which make use of various historical statistics when training deep neural networks. The discussions with related topics like recurrent/memory networks, ensemble learning, and reinforcement learning are demonstrated. We also expose future challenges of this topic and encourage the community to pay attention to the think of historical learning principles when designing algorithms. The paper list related to historical learning is available at <a href="https://github.com/Martinser/Awesome-Historical-Learning">https://github.com/Martinser/Awesome-Historical-Learning</a>.</p>

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A survey of historical learning: learning models with learning history

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月9日

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  • Xiang Li,
  • Ge Wu,
  • Lingfeng Yang,
  • Wenhai Wang,
  • Renjie Song,
  • Ming-Ming Cheng,
  • Jian Yang

摘要

New knowledge originates from the old. The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models. In this survey, we comprehensively review and summarize the topic–“Historical Learning: Learning Models with Learning History”, which learns better neural models with the help of their learning history during its optimization, from three detailed aspects: Historical Type (what), Utility Part (where) and Storage Form (how). To our best knowledge, it is the first survey that systematically studies the methodologies which make use of various historical statistics when training deep neural networks. The discussions with related topics like recurrent/memory networks, ensemble learning, and reinforcement learning are demonstrated. We also expose future challenges of this topic and encourage the community to pay attention to the think of historical learning principles when designing algorithms. The paper list related to historical learning is available at https://github.com/Martinser/Awesome-Historical-Learning.