# Continual Classification Learning Using Generative Models

arXiv: https://arxiv.org/abs/1810.10612 · HTML: https://mlsanity.com/paper/1810.10612/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

## Built on by (most cited first, 28 of 28)

- [Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters](https://mlsanity.com/paper/2403.11549/) (2024)
- [Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models](https://mlsanity.com/paper/2303.06628/) (2023)
- [Don't Stop Learning: Towards Continual Learning for the CLIP Model](https://mlsanity.com/paper/2207.09248/) (2022)
- [An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions](https://mlsanity.com/paper/2204.08817/) (2022)
- [Continual Forgetting for Pre-trained Vision Models](https://mlsanity.com/paper/2403.11530/) (2024)
- [MLLM-CL: Continual Learning for Multimodal Large Language Models](https://mlsanity.com/paper/2506.05453/) (2025)
- [Online Continual Learning of End-to-End Speech Recognition Models](https://mlsanity.com/paper/2207.05071/) (2022)
- [Continual Learning on Graphs: A Survey](https://mlsanity.com/paper/2402.06330/) (2024)
- [LLMs Can Evolve Continually on Modality for X-Modal Reasoning](https://mlsanity.com/paper/2410.20178/) (2024)
- [LADA: Scalable Label-Specific CLIP Adapter for Continual Learning](https://mlsanity.com/paper/2505.23271/) (2025)
- [Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models](https://mlsanity.com/paper/2102.08355/) (2021)
- [The Importance of Robust Features in Mitigating Catastrophic Forgetting](https://mlsanity.com/paper/2306.17091/) (2023)
- [CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering](https://mlsanity.com/paper/2211.10567/) (2022)
- [On Sequential Bayesian Inference for Continual Learning](https://mlsanity.com/paper/2301.01828/) (2023)
- [CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection](https://mlsanity.com/paper/2204.05220/) (2022)
- [Class Impression for Data-free Incremental Learning](https://mlsanity.com/paper/2207.00005/) (2022)
- [Enhanced Few-Shot Class-Incremental Learning via Ensemble Models](https://mlsanity.com/paper/2401.07208/) (2024)
- [Integral Continual Learning Along the Tangent Vector Field of Tasks](https://mlsanity.com/paper/2211.13108/) (2022)
- [Adiabatic replay for continual learning](https://mlsanity.com/paper/2303.13157/) (2023)
- [I3DOD: Towards Incremental 3D Object Detection via Prompting](https://mlsanity.com/paper/2308.12512/) (2023)
- [Decomposing and Composing: Towards Efficient Vision-Language Continual Learning via Rank-1 Expert Pool in a Single LoRA](https://mlsanity.com/paper/2601.22828/) (2026)
- [Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts](https://mlsanity.com/paper/2108.10781/) (2021)
- [CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning](https://mlsanity.com/paper/2406.05631/) (2024)
- [LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning](https://mlsanity.com/paper/2507.13568/) (2025)
- [φ-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models](https://mlsanity.com/paper/2602.22601/) (2026)
- [Brain-Inspired Continual Learning-Robust Feature Distillation and Re-Consolidation for Class Incremental Learning](https://mlsanity.com/paper/2404.14588/) (2024)
- [A Methodology-Oriented Study of Catastrophic Forgetting in Incremental Deep Neural Networks](https://mlsanity.com/paper/2405.08015/) (2024)
- [Continual Cross-Modal Generalization](https://mlsanity.com/paper/2504.00561/) (2025)

## 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).