Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

11 papers of 6,984Sort Recent · Most cited
  1. 2026
    A 22nm Continual Learning Accelerator for Autonomous Systems with 69.2TOPS/W Dynamic-Sparse-Weight-Updat and Dual-Mode Vector-Scaled-INT4 ProcessingJunnosuke Suzuki, Takuma Ishibashi, Hikari Otsuka … Masato MotomuraIEEE Custom Integrated Circuits Conference (CICC) · Hokkaido University
  2. 2026
    Adversarial Perturbation Shield: Preventing Concept Bleed-through in Continual Learning of Personalized Generative ModelsZiwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki HaseyamaAAAI · Hokkaido University
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  3. 2026
    Deep Generative Replay-Based Personalization With Conditional Latent Attention for Diffusion ModelsHaruka Matsuda, Ren Togo, Keisuke Maeda … Miki HaseyamaIEEE Access · Hokkaido University
  4. 2025
    Rethinking Continual Learning with Pre-Trained Models: Knowledge-Preserving ApproachMakoto Misaizu, Koshi Watanabe, Keisuke Maeda … Miki HaseyamaIEEE 14th Global Conference on Consumer Electronics (GCCE) · Hokkaido University
  5. 2025
    Analysis of Model Merging for Open-Vocabulary Models with Parameter Efficient Fine-Tuning Leveraging Distributed DataKenta Kubota, Ren Togo, Keisuke Maeda … Miki HaseyamaIEEE International Conference on Consumer Electronics - T… · Hokkaido University of Science · Hokkaido University
  6. 2025
    Analysis of Model Merging Methods for Continual Updating of Foundation Models in Distributed Data SettingsKenta Kubota, Ren Togo, Keisuke Maeda … Miki HaseyamaApplied Sciences · Hokkaido University
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  7. 2024
    Introducing Class Replacement Technique in Class Incremental Learning in Generative ModelsTaro Togo, Ren Togo, Keisuke Maeda … Miki HaseyamaInternational Conference on Consumer Electronics - Taiwan… · Hokkaido University
  8. 2024
    Analysis of Continual Learning Techniques for Image Generative Models with Learned Class Information ManagementTaro Togo, Ren Togo, Keisuke Maeda … Miki HaseyamaSensors · Hokkaido University
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  9. 2024
    Multi-Object Editing in Personalized Text-To-Image Diffusion Model Via Segmentation GuidanceHaruka Matsuda, Ren Togo, Keisuke Maeda … Miki HaseyamaICASSP · Hokkaido University
  10. 2023
    Text-to-image Diffusion Model Suppressing Catastrophic Forgetting via Elastic Weight ConsolidationHaruka Matsuda, Ren Togo, Keisuke Maeda … Miki HaseyamaIEEE 12th Global Conference on Consumer Electronics (GCCE) · Hokkaido University
  11. 2016
    Analytical Incremental Learning: Fast Constructive Learning Method for Neural NetworkSyukron Abu Ishaq Alfarozi, Noor Akhmad Setiawan, Teguh Bharata Adji … Masanori SugimotoSpringer LNCS · Universitas Gadjah Mada · King Mongkut's Institute of Technology Ladkrabang · +1
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.