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Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Updated: Jan 8, 2026

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CSCL：連続教師あり対照学習における可塑性と安定性のギャップを埋める

Yi Xiong1, Liqi Xiang2, Qianyue Cao1

  • 1School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, China.

Neural networks : the official journal of the International Neural Network Society
|December 21, 2025
PubMed
まとめ

本研究では、可塑性と安定性を強化することにより、継続学習（CL）を改善するための継続的教師あり対照学習（CSCL）を導入します。CSCLは、非定常データストリームでのパフォーマンスを向上させるための新しい方法を使用します。

キーワード:
破滅的忘却継続学習対照学習表現知識

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科学分野:

  • 人工知能
  • 機械学習
  • コンピュータビジョン

背景:

  • 継続学習（CL）は、非定常データストリームに対処し、モデルが以前の知識を忘れることなく新しい情報を学習できるようにします。
  • 教師あり対照学習（SCL）は、特に忘却に対する耐性を向上させる上で、CLパフォーマンスを向上させる上で有望であることが示されています。
  • しかし、SCLベースのCLモデルは、表現空間における学習の可塑性とメモリの安定性に関して依然として課題に直面しています。

主な方法:

  • 継続的教師あり対照学習（CSCL）フレームワークを導入しました。
  • 冗長除去補間法を組み込み、新しいクラスのネガティブサンプルの多様性と学習の可塑性を向上させました。
  • 磁力法を実装して、クラス間の分離とクラス内の集合を確保し、古いクラスのメモリの安定性を向上させました。

結論:

  • CSCLは、継続学習の設定において、学習の可塑性とメモリの安定性の両方を効果的に強化します。
  • 提案された冗長除去補間法と磁力法は、大幅な改善を提供し、SCLベースのCLの適応可能なコンポーネントです。
  • CSCLは、モデルが以前に取得した知識を保持しながら継続的に学習できるようにするための重要な進歩を表しています。