The paper introduces sLoTh, a parameter‑efficient continual learning framework for sparse event‑based vision transformers. sLoTh freezes the backbone and limits plasticity to low‑rank attention updates (seLoRA) and shared neuronal threshold modulation, updating less than 1% of parameters without replay buffers. Experiments on CIFAR‑100, Tiny‑ImageNet, ImageNet‑100, and ImageNet‑R show competitive rehearsal‑free performance across up to 100 tasks while achieving roughly 6.5× lower energy consumption than dense vision transformers.
By Vaishnavi Nagabhushana, Kartikay Agrawal, Ayon Borthakur
arXiv:2606. 20031v1 Announce Type: cross Abstract: Dynamic environmental changes, confined workspaces, and stringent real-time constraints make pathfinding in Robotic Mobile Fulfillment Systems (RMFS) a challenging problem for conventional search- and rule-based methods, which typically suffer from high computational complexity and long decision latency.
By Junzhe Xu, Zecui Zeng, Lusong Li, Yuetong Fang, Renjing Xu
arXiv:2411. 16073v4 Announce Type: replace-cross Abstract: Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-TransFormers (Soft-TF), a continual learning framework that adapts a frozen pre-trained Transformer through task-specific soft subnetworks: real-valued multiplicative masks over the query, key, value, and output projections of selected self-attention layers.
By Haeyong Kang, Chang D. Yoo
The paper introduces sLoTh, a parameter‑efficient continual learning framework for sparse event‑based vision transformers. By freezing the backbone and limiting plasticity to low‑rank attention updates (seLoRA) and shared neuronal threshold modulation, sLoTh adapts to new tasks while updating less than 1% of the parameters and avoiding replay buffers. Experiments on CIFAR‑100, Tiny‑ImageNet, ImageNet‑100, and ImageNet‑R show competitive rehearsal‑free performance across up to 100 tasks and achieve roughly 6.5× lower energy consumption than dense vision transformers.
RobustNCL is a lightweight framework that enhances the reliability of neuromorphic continual learning (NCL) systems against transient hardware faults. It detects faults during weight access from off‑chip memory and cures affected weights using one of three strategies—zero replacement, min/max value, or most‑probable value—storing curing values in radiation‑hardened memory. Experiments on Split MNIST, Split Fashion‑MNIST, Split CIFAR‑10, and Split CIFAR‑100 show that RobustNCL maintains accuracy within 0.5% of fault‑free models and significantly outperforms baseline NCL methods while adding negligible latency and energy overhead.
By Rachmad Vidya Wicaksana Putra, Shuochen Zhou, Muhammad Shafique
arXiv:2608. 04358v1 Announce Type: new Abstract: Continual learning (CL) requires models to learn tasks sequentially, yet deep neural networks often suffer from plasticity loss and poor knowledge transfer, which can impede their long-term adaptability.
By Seyed Roozbeh Razavi Rohani, Khashayar Khajavi, Wesley Chung, Mandana Samiei, Mo Chen
arXiv:2511.01553v3 Announce Type: replace-cross
Abstract: Edge AI must adapt to new classes and shifting data within a tight power budget, but online continual learning (learning from each sample bef...
By Elvin Hajizada, Danielle Rager, Timothy Shea, Leobardo Campos-Macias, Andreas Wild, Eyke H\"ullermeier, Yulia Sandamirskaya, Mike Davies
arXiv:2609.17026v1 Announce Type: new
Abstract: Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge to incrementally learn tasks from a da...
By Yunxiang Fu, Meng Lou, Zicheng Liao, Yizhou Yu
arXiv:2504. 01219v2 Announce Type: replace Abstract: Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting.
By Grzegorz Rype\'s\'c
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...
By Yunxiang Fu, Meng Lou, Yizhou Yu
arXiv:2609.38833v1 Announce Type: new
Abstract: Federated continual learning must integrate new tasks over time without losing earlier-task knowledge. Most existing methods attach an anti-forgetting...
By Sungmin Kang, Zhengzhong Tu, Sunwoo Lee
arXiv:2608. 01252v1 Announce Type: new Abstract: Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks.
By Pengxiang Wang, Hongbo Bo, Jun Hong, Weiru Liu, Kedian Mu