Storage-Efficient Continual Learning

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9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
Oct 13, 2026Francesco Panetta, Federico Di Valerio, Michela ProiettiStorage-Efficient Continual Learning Replay-Based Continual Learning
Sapienza University of Rome, Italy · Goethe University Frankfurt, Germany
Static Gradient Attribution Underperforms a Density-Matched Random Mask on Loss-Based Forgetting Within LoRA B-Matrix
Oct 8, 2026Yu Sun, Junwei Zhou, Zuodong Xiang +5Storage-Efficient Continual Learning Continual Learning for LLMs
University of Pennsylvania · Matter Innovation Inc. · University of California, Davis +5
CE $^4$ L: Continual Ego, Exo, and Ego-Exo Learning
Sep 20, 2026Hongwei Yan, Kanglei Zhou, Yuchen Liu +3Temporal Action Segmentation Next-Action Prediction
School of Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China · Tsinghua-Peking Center for Life Sciences · Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China +2
Neural Subspace Reallocation: Continual Learning as Retrieval-Based Subspace Memory Management
Jun 29, 2026Byeong Hoon YoonStorage-Efficient Continual Learning Continual Learning
Independent Researcher
Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning
May 31, 2026Anushka Tiwari, Kaiyi JiStorage-Efficient Continual Learning Parameter-Efficient Fine-Tuning
Institute for Artificial Intelligence and Data Science, University at Buffalo, Buffalo, NY, USA · Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA
Revitalizing the Beginning: Avoiding Storage Dependency for Model Merging in Continual Learning
May 8, 2026Xi Wang, Cheng DengStorage-Efficient Continual Learning Continual Learning
School of Electronic Engineering, Xidian University, Xi’an, Shaanxi, China.
Temporal Memory for Resource-Constrained Agents: Continual Learning via Stochastic Compress-Add-Smooth
Mar 31, 2026Michael ChertkovMemory Compression Continual Learning
Graduate Inter-Disciplinary Program in Applied Mathematics, & Department of Mathematics, University Arizona, Tucson, AZ 85721
Pruned Adaptation Modules: A Simple yet Strong Baseline for Continual Foundation Models
Mar 22, 2026Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin VanschorenStorage-Efficient Continual Learning Class-Incremental Learning
AMOR/e Lab, Eindhoven University of Technology
Grow, Assess, Compress: Adaptive Backbone Scaling for Memory-Efficient Class Incremental Learning
Mar 9, 2026Adrian Garcia-Castañeda, Jon Irureta, Jon Imaz +1Storage-Efficient Continual Learning Class-Incremental Learning
Ikerlan Technology Research Center, Arrasate, Spain · Goi Eskola Politeknikoa, Mondragon Unibertsitatea, Arrasate, Spain · University of the Basque Country UPV/EHU, Spain

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Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks

Francesco Panetta, Federico Di Valerio, Michela Proietti

Sapienza University of Rome, Italy · Goethe University Frankfurt, Germany

Storage-Efficient Continual Learning Replay-Based Continual Learning Spiking Neural Networks Incremental Learning

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Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentially. This work investigates the integration of Experience Packing and Replay (EPR) as a memory-efficient continual learning strategy for deep SNNs under low-latency constraints. The proposed framework combines Batch Normalization Through Time stabilized VGG9 SNNs with Spike Activation Maps (SAM), which provide forward-only explanations based on spike activity. These explanations are temporally aggregated into spatial attention maps and used to select the most informative image patches for replay, instead of storing full images. Experiments on Split CIFAR10 and Split CIFAR100 show that SAM-guided EPR improves the accuracy-memory trade-off under tight memory budgets, and remains competitive while requiring substantially less storage.