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Continual Classification Learning Using Generative Models

arXiv 1810.10612 ↗First posted 24 Oct 2018cs.LG stat.MLcited by 28

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Abstract (arXiv)

Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on previously learned tasks when tasks are presented one at a time. This problem is called catastrophic forgetting. In this work, we propose a classification model that learns continuously from sequentially observed tasks, while preventing catastrophic forgetting. We build on the lifelong generative capabilities of [10] and extend it to the classification setting by deriving a new variational bound on the joint log likelihood, log p(x; y).

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2018.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 5; 2024: 8; 2025: 4; 2026: 2.

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  1. 2024Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapterscited by 199
  2. 2023Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Modelscited by 112
  3. 2022Don't Stop Learning: Towards Continual Learning for the CLIP Modelcited by 61
  4. 2022An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditionscited by 54
  5. 2024Continual Forgetting for Pre-trained Vision Modelscited by 29
  6. 2025MLLM-CL: Continual Learning for Multimodal Large Language Modelscited by 21
  7. 2022Online Continual Learning of End-to-End Speech Recognition Modelscited by 15
  8. 2024LLMs Can Evolve Continually on Modality for X-Modal Reasoningcited by 12
  9. 2024Continual Learning on Graphs: A Surveycited by 11
  10. 2025LADA: Scalable Label-Specific CLIP Adapter for Continual Learningcited by 11
  11. 2021Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Modelscited by 9
  12. 2023The Importance of Robust Features in Mitigating Catastrophic Forgettingcited by 7
16 more
  1. 2022CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answeringcited by 6
  2. 2023On Sequential Bayesian Inference for Continual Learningcited by 6
  3. 2022CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detectioncited by 5
  4. 2022Class Impression for Data-free Incremental Learningcited by 4
  5. 2024Enhanced Few-Shot Class-Incremental Learning via Ensemble Modelscited by 4
  6. 2022Integral Continual Learning Along the Tangent Vector Field of Taskscited by 3
  7. 2023Adiabatic replay for continual learningcited by 3
  8. 2023I3DOD: Towards Incremental 3D Object Detection via Promptingcited by 3
  9. 2021Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecastscited by 2
  10. 2024CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learningcited by 2
  11. 2025LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learningcited by 2
  12. 2026φ-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Modelscited by 2
  13. 2026Decomposing and Composing: Towards Efficient Vision-Language Continual Learning via Rank-1 Expert Pool in a Single LoRAcited by 1
  14. 2024Brain-Inspired Continual Learning-Robust Feature Distillation and Re-Consolidation for Class Incremental Learningcited by 0
  15. 2024A Methodology-Oriented Study of Catastrophic Forgetting in Incremental Deep Neural Networkscited by 0
  16. 2025Continual Cross-Modal Generalizationcited by 0