Lifelong Representations：它和通用视觉自监督的关系在于：系统梳理 continual self-supervised learning for vision，适合作为长期预训练/持续学习路线图
高相关；详见方法、贡献和实验边界。
Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Models arXiv new + IEEE EAIS 2026 原文链接
编号2607.09785优先级P1类别arXiv new + IEEE EAIS 2026会议arXiv new + IEEE EAIS 2026方法系统梳理 continual self-supervised learning for vision，适合作为长期预训练/持续学习路线图来源arXiv / OpenReview
先说结论。它和通用视觉自监督的关系在于：系统梳理 continual self-supervised learning for vision，适合作为长期预训练/持续学习路线图。 高相关；详见方法、贡献和实验边界。
Figureure 3 · : The diagram illustrates the different approaches to CSSL: (A) A distFig. 3: The diagram illustrates the different approaches to CSSL: (A) A distillation loss $( \mathcal { L } ^ { \mathrm { { D i s } } } )$ is used to align representations of the current model with those of the previous one $( T - 1 )$ with an optional projector; (B) a weight regularization loss $( \mathcal { L } ^ { \mathrm { R e g } } )$ penalizes changes to parameters that were important for past tasks, computed by comparing the current model (T ) against the frozen previous model $( T - 1 ) \colon$ ; (C) incoming data at step T consists of new images combined with a replay memory buffer of past samples; (D) a parameter-isolated architecture separates frozen and trainable components, enabling taskspecific plasticity while preserving prior knowledge; (E) a model merging module combines the previous and current model checkpoints; (F) the overall training objective optionally adapts $( \mathcal { L } ^ { \mathrm { { a d } } } )$ the self-supervised loss $( \mathcal { L } ^ { \mathrm { { S S L } } } )$ to embed continual learning constraints directly into the SSL objective.这张图概括 Lifelong Representations 的整体方法流程。阅读时先看模块之间传递的训练信号，再看作者如何把目标拆成可优化的子问题。Figureure 1 · : A conceptual visualization of CSSLFig. 1: A conceptual visualization of CSSL. The bottom part represents tasks T arriving in sequence, and the top part shows an illustration of an SSL objective (in this example, non-contrastive).这张可视化用来解释 Lifelong Representations 学到的中间表征或对齐关系。重点看它是否支持正文里的机制判断。
核心问题
它和通用视觉自监督的关系在于：系统梳理 continual self-supervised learning for vision，适合作为长期预训练/持续学习路线图。
方法拆解
系统梳理 continual self-supervised learning for vision，适合作为长期预训练/持续学习路线图
