English(EN)Continual Visual Learning under Evolving Semantic Concept Shift
新研究探索人工智能模型持续学习的先进技术 · 追踪8个来源
作者PulseAugur 编辑部·[12 个来源]·
研究人员正在开发持续学习的新方法，旨在使人工智能模型能够在不忘记先前获得的知识的情况下学习新信息。一种方法，“具有自组织映射和合成重放的类增量持续学习”，利用自组织映射（SOMs）和合成数据生成在CIFAR-10和CIFAR-100等基准测试中取得有竞争力的性能。另一篇论文，“具有增长自组织映射和合成重放的无监督持续学习”，通过无监督方式使用增长SOMs（GSOMs）扩展了这一点，即使与监督方法相比也显示出强大的结果。此外，还提出了一个名为FiUni的框架，用于大型语言模型中的无任务持续学习，利用Fisher信息矩阵属性，通过LoRA等参数高效方法来检测和适应新任务。最后，“用于持续学习的快速权重注意力”探索了循环快速权重记忆和选择性状态空间模型，以提高序列模型中的上下文压缩和长度外推能力。
AI
arXiv:2608.29513v1 Announce Type: cross Abstract: Machine unlearning enables deep neural networks to selectively remove the influence of specific data in response to privacy and regulatory requirements. While prior work largely studies single-shot unlearning, real-world systems m…
arXiv:2608.31096v1 Announce Type: cross Abstract: Class-incremental learning (CIL) requires a model to incrementally learn tasks that contain new classes without accessing earlier training data while preserving the ability to recognize all seen classes. Recently, pretrained-model…
arXiv cs.NE (Neural & Evolutionary)
TIER_1English(EN)·Noorbakhsh Amiri Golilarz·
Vision Transformers provide strong visual representations but typically rely on slowly updated parameters, limiting their ability to organize newly acquired information across different memory timescales. This work proposes \textit{Hierarchical Hebbian Memory}, a three-level memo…
arXiv cs.LG
TIER_1English(EN)·Pujan Thapa, Alexander Ororbia, Travis Desell·
arXiv:2608.27662v1 Announce Type: new Abstract: This work presents a generative continual learning framework based on growing self-organizing maps (GSOMs) that are augmented with learned distributional statistics as well as encoder-decoder models for class-incremental learning. T…
arXiv cs.LG
TIER_1English(EN)·Pujan Thapa, Alexander Ororbia, Travis Desell·
arXiv:2508.21240v2 Announce Type: replace Abstract: This work introduces a novel generative continual learning framework based on self-organizing maps (SOMs), a brain-inspired natural computing model, extended with learned distributional statistics and encoder--decoder models for…
arXiv:2608.27070v1 Announce Type: cross Abstract: To mitigate catastrophic forgetting in downstream continual learning (CL) for large language models (LLMs), existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, these metho…
To mitigate catastrophic forgetting in downstream continual learning (CL) for large language models (LLMs), existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, these methods often rely on explicit task boundaries during t…
Recurrent fast-weight memories and selective state-space models are analyzed as online learning rules under autoregressive semantics, yielding normalized update families with stable renormalization that improve length extrapolation.
arXiv:2608.23782v1 Announce Type: new Abstract: Continual learning faces the persistent challenge of catastrophic forgetting, where sequential task updates degrade previously acquired knowledge. While prompt-based methods integrated with pre-trained models offer a compelling solu…
Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the prediction task remains fixed. In long-lived visual systems, however, taxonomies, policies, and concept definitions can themselves …
arXiv cs.CV
TIER_1English(EN)·Mohammed Yusuf Mujawar, Noorbakhsh Amiri Golilarz·
arXiv:2609.00358v1 Announce Type: new Abstract: Vision Transformers provide strong visual representations but typically rely on slowly updated parameters, limiting their ability to organize newly acquired information across different memory timescales. This work proposes \textit{…
arXiv stat.ML
TIER_1English(EN)·Yifan Zhang, Steve Ta, Jasper Zhang, Jichen Feng, Shuzhen Li, Yongxin Zhang, Yifeng Liu, Huizhuo Yuan, Mengdi Wang, Quanquan Gu, Andrew Chi-Chih Yao·
arXiv:2608.27763v1 Announce Type: cross Abstract: Recurrent fast-weight memories and selective state-space models compress an expanding context into a fixed-size recurrent state, making the state transition an online learning rule. We study this rule under read-after-write autore…
