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insight - Contrastive Continual Learning
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation
The author proposes Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions and introduces the Prototype-instance Relation Distillation (PRD) loss to maintain relationships between prototypes and sample representations. The core reasoning behind the content is to address Catastrophic Forgetting in continual learning by utilizing importance sampling and PRD to enhance knowledge preservation.
重要サンプリングとプロトタイプ-インスタンス関係の蒸留を用いた比較的継続的な学習
高品質な表現を持つ比較的学習方法に焦点を当て、従来の連続設定での壊滅的忘却問題を回避するために、知識保存と Catastrophic Forgetting の効果的な対策を提案します。
중요한 정보를 숨기지 않고 Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation에 대한 분석
고품질 표현을 통한 Catastrophic Forgetting 극복을 위한 Contrastive Continual Learning의 효과적인 방법론 소개
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