arXiv AI July 22, 2026 By Simone Milani

Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification

arXiv:2607. 19122v1 Announce Type: cross Abstract: Continual learning (CL) has been recently employed in biometric identification systems thanks to its ability to integrate new knowledge within a pre-trained model and to the possibility of reducing the computational cost of training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 12

MOSAIC: Modality-Specific Adaptation for Incremental Continual Learning in Parkinson's Disease Gait Assessment

arXiv:2606. 13258v1 Announce Type: new Abstract: Gait-based Parkinson's disease assessment increasingly relies on heterogeneous sensors, but clinical systems rarely collect all modalities simultaneously.

By Minlin Zeng, Zhipeng Zhou, Yang Qiu, Martin J. McKeown, Zhiqi Shen

efficiency multimodal safety

arXiv Machine Learning

Jul 30

Gated Adaptation for Continual Learning in Human Activity Recognition

arXiv:2603. 10046v2 Announce Type: replace Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR).

By Reza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu, Edison Thomaz, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh

arXiv AI

Jun 8

Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning

arXiv:2505. 12239v2 Announce Type: replace-cross Abstract: In Continual Learning (CL), using a Pre-Trained Model (PTM) as the feature extractor has become a popular practice.

By Yajiang Huang, Jianheng Tang, Kejia Fan, Huiping Zhuang, Anfeng Liu, Tian Wang, Yunhuai Liu, Mianxiong Dong, Houbing Herbert Song

benchmarks safety

arXiv Machine Learning

Jul 10

Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning

arXiv:2511. 08226v2 Announce Type: replace Abstract: In order to achieve Continual Learning (CL), the problem of catastrophic forgetting, one that has plagued neural networks since their inception, must be overcome.

By Rapha\"el Bayle, Martial Mermillod, Robert M. French

benchmarks

arXiv Machine Learning

Aug 3

TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners

arXiv:2607. 29592v1 Announce Type: cross Abstract: The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks.

By Mostafa ElAraby, Samer B. Nashed, Liam Paull

arXiv Machine Learning

Jul 30

The Art of Not Forgetting A Local Learning Architecture for Continual Learning

arXiv:2607. 26523v1 Announce Type: new Abstract: We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre?

By Ashmith Atmuri, Yashaswini Rao Bhogarajula

llms benchmarks

Related stories

MOSAIC: Modality-Specific Adaptation for Incremental Continual Learning in Parkinson's Disease Gait Assessment

Gated Adaptation for Continual Learning in Human Activity Recognition

Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning

Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning

TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners

The Art of Not Forgetting A Local Learning Architecture for Continual Learning