Storage-Efficient Continual Learning

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18 papers8 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Drift-adaptive Representation Preservation for Domain-Incremental Learning
Nov 4, 2026Hyunmin Kong, Gyutae Oh, Jitae ShinDomain-Incremental Learning Regularization-Based Continual Learning
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
DLER: Dual-Logit Experience Replay for Online Continual Learning
Nov 3, 2026Huiwei Lin, Shanshan Feng, Kong Aik LeeOnline Continual Learning Replay-Based Continual Learning
The Hong Kong Polytechnic University, Hong Kong SAR, China · Wuhan University, Wuhan 430072, China
RASP: Read-All, Sparse-Plasticity Continual Learning in a Stationary-Size LoRA
Nov 2, 2026Wen-Hsuan Liu, Amara Okonkwo, Tom Saliencro +2Continual LoRA Storage-Efficient Continual Learning
University of California, Irvine · University of Washington
Class-aware Latent Attention Replay (CLAR): Class Incremental Hazard Diagnosis for Resilient AI in the Manufacturing Industrial Internet
Oct 29, 2026Avranshu Mahanty, Ran Jin, Yingyan ZengReplay-Based Continual Learning Class-Incremental Learning
Department of Mechanical, Materials, and Industrial Engineering, University of Cincinnati, Cincinnati, OH · Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA
TRACE: Memory-Efficient Time Series Class-Incremental Learning via Temporal-Spectral Reconstruction
Oct 26, 2026Junhui Mao, Junru Zhang, Liming Ye +7Class-Incremental Learning Storage-Efficient Continual Learning
Zhejiang University, Hangzhou, China · Nanyang Technological University, Singapore · Ant Group, China
WiMoT: Replay-Free Continual Wi-Fi Sensing via Moment Transport
Oct 23, 2026Chunchun Yang, Yang Cao, Zhitang ChenContinual Learning Wi-Fi Sensing
School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China. · Z. Chen is an Independent Researcher.
HERD: Harness-Evolving Recursive Distillation for Continual Learning
Oct 22, 2026Xun Sun, Thanh Thi Nguyen, Thuy-Trang VuContinual Learning Continual Learning for LLM Agents
Monash University · University of the Sunshine Coast
Learning without Overwriting: Structural Knowledge Fusion for Continual Learning
Oct 18, 2026Kanishk Bakshi, Amitansu Pattanaik, Kathiravan SrinivasanClass-Incremental Learning Incremental Learning
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India · Defence Geoinformatics Research Establishment, Defence Research and Development Organisation, New Delhi 110054, India
SARAM: Sparse Adapter Allocation with Reuse and Merging for Lifelong Robot Learning
Oct 17, 2026SeongJun Jeong, Chungwoo Lee, Gi-Cheon Kang +1Adapter-Based Continual Learning Continual Robot Learning
Seoul National University · Ajou University
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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Drift-adaptive Representation Preservation for Domain-Incremental Learning

Hyunmin Kong, Gyutae Oh, Jitae Shin

Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

Domain-Incremental Learning Regularization-Based Continual Learning Storage-Efficient Continual Learning

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Pretrained vision models provide strong general-purpose representations, but in Domain-Incremental Learning (DIL), where the input distribution changes sequentially, adapting to new domains often degrades performance on previously learned ones. We observe that forgetting is not uniform across domains and classes and is not explained by training order alone. This motivates us to track each past domain-class concept individually. We use representation drift, defined as the deviation of a past concept's current representation from a fixed anchor, as a measurable surrogate for forgetting risk. The measured drift then serves two complementary roles. A consolidation loss assigns stronger preservation to high-drift pairs, while prototype construction down-weights highly drifted domains to reduce their influence on the class reference. Preservation is thus allocated by measured drift rather than assumed importance. To obtain this signal during training without storing past raw data, we repurpose a Joint-Embedding Predictive Architecture (JEPA). For each domain-class pair, we store only a compact context summary in the backbone feature space and rerun it through the learned context-to-target relation to measure its drift from the fixed anchor. Across DomainNet, Office-Home, CORe50, and CDDB-Hard, our method sustains a strong accuracy-forgetting balance on every benchmark, using substantially less training memory than accuracy-competitive methods and the smallest storage among compared methods.