Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

24 papers of 8,653Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2026PDF ↗
  3. 2026
    SPREAD: Subspace Representation Distillation for Lifelong Imitation LearningKaushik Roy, Giovanni D'urso, Nicholas Lawrance … Peyman MoghadamarXiv
    PDF ↗
  4. 2026
    ELLA: Efficient Lifelong Learning for Adapters in Large Language ModelsShristi Das Biswas, Yue Zhang, Anwesan Pal … Kaushik RoyEACL
    PDF ↗
  5. 2024
    CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningMarco Paul E. Apolinario, Sakshi Choudhary, Kaushik RoyICCV · Purdue University West Lafayette
    PDF ↗
  6. 2025PDF ↗
  7. 2024
    M2Distill: Multi-Modal Distillation for Lifelong Imitation LearningKaushik Roy, Akila Dissanayakc, Brendan Tidd, Pcyman MoghadamICRA · Commonwealth Scientific and Industrial Research Organisation · Data61
    PDF ↗
  8. 2025
  9. 2024
    A collective AI via lifelong learning and sharing at the edgeAndrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman … Soheil KolouriNature Machine Intelligence · Loughborough University · Rice University · +21
  10. 2023
    L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models✱Aidin Shiri, Kaushik Roy, Amit Sheth, Manas GaurJoint International Conference on Data Science & Mana… · University of Maryland, Baltimore County · University of South Carolina
    PDF ↗
  11. 2024
    Continual Learning: A Review of Techniques, Challenges, and Future DirectionsBuddhi Wickramasinghe, Gobinda Saha, Kaushik RoyIEEE TAI · Purdue University West Lafayette
  12. 2023
    CL3: Generalization of Contrastive Loss for Lifelong LearningKaushik Roy, Christian Simon, Peyman Moghadam, Mehrtash HarandiJournal of Imaging · Commonwealth Scientific and Industrial Research Organisation · Data61 · +3
    PDF ↗
  13. 2023
    Subspace Distillation for Continual LearningKaushik Roy, Christian Simon, Peyman Moghadam, Mehrtash HarandiNeural Networks
    PDF ↗
  14. 2023
    L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceKaushik Roy, Peyman Moghadam, Mehrtash HarandiMICCAI
    PDF ↗
  15. 2023
    Online continual learning with saliency-guided experience replay using tiny episodic memoryGobinda Saha, Kaushik RoyMachine Vision and Applications · Purdue University West Lafayette
  16. 2023
    CoDeC: Communication-Efficient Decentralized Continual LearningSakshi Choudhary, Sai Aparna Aketi, Gobinda Saha, Kaushik RoyTrans. Mach. Learn. Res.
    PDF ↗
  17. 2023PDF ↗
  18. 2021
    Saliency Guided Experience Packing for Replay in Continual LearningGobinda Saha, Kaushik RoyWACV · Purdue University West Lafayette
    PDF ↗
  19. 2020
    SPACE: Structured Compression and Sharing of Representational Space for Continual LearningGobinda Saha, Isha Garg, Aayush Ankit, Kaushik RoyIEEE Access · Purdue University West Lafayette
    PDF ↗
  20. 2019PDF ↗
  21. 2017
    Incremental Learning in Deep Convolutional Neural Networks Using Partial Network SharingSyed Shakib Sarwar, Aayush Ankit, Kaushik RoyIEEE Access · Purdue University West Lafayette
    PDF ↗
  22. 2018
    Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learningDeboleena Roy, Priyadarshini Panda, Kaushik RoyNeural Networks · Purdue University West Lafayette
    PDF ↗
  23. 2017
    ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural NetworksPriyadarshini Panda, Jason M. Allred, Shriram Ramanathan, Kaushik RoyIEEE Journal on Emerging and Selected Topics in Circuits… · Purdue University West Lafayette
    PDF ↗
  24. 2017
    Habituation based synaptic plasticity and organismic learning in a quantum perovskiteFan Zuo, Priyadarshini Panda, Michele Kotiuga … Shriram RamanathanNature Communications · Purdue University West Lafayette · Rutgers, The State University of New Jersey · +3
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 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.