# Learning to Prompt Knowledge Transfer for Open-World Continual Learning

arXiv: https://arxiv.org/abs/2312.14990 · HTML: https://mlsanity.com/paper/2312.14990/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Adversarial Continual Learning](https://mlsanity.com/paper/2003.09553/) (2020)
- [Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation](https://mlsanity.com/paper/2012.06575/) (2020)
- [Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy](https://mlsanity.com/paper/1908.04951/) (2019)
- [Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks](https://mlsanity.com/paper/2112.10017/) (2021)
- [Open-World Continual Learning: Unifying Novelty Detection and Continual Learning](https://mlsanity.com/paper/2304.10038/) (2023)
- [Forward and Backward Knowledge Transfer for Sentiment Classification](https://mlsanity.com/paper/1906.03506/) (2019)

## Built on by (most cited first, 15 of 15)

- [Personalized Federated Continual Learning via Multi-granularity Prompt](https://mlsanity.com/paper/2407.00113/) (2024)
- [DAM: Dynamic Adapter Merging for Continual Video QA Learning](https://mlsanity.com/paper/2403.08755/) (2024)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor](https://mlsanity.com/paper/2412.18355/) (2024)
- [Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping](https://mlsanity.com/paper/2502.20032/) (2025)
- [Multi-granularity Knowledge Transfer for Continual Reinforcement Learning](https://mlsanity.com/paper/2401.15098/) (2024)
- [ErrorEraser: Unlearning Data Bias for Improved Continual Learning](https://mlsanity.com/paper/2506.09347/) (2025)
- [ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classification](https://mlsanity.com/paper/2507.11845/) (2025)
- [Exploring Open-world Continual Learning with Knowns-Unknowns Knowledge Transfer](https://mlsanity.com/paper/2502.20124/) (2025)
- [Improving Open-world Continual Learning under the Constraints of Scarce Labeled Data](https://mlsanity.com/paper/2502.20974/) (2025)
- [The Lie of the Average: How Class Incremental Learning Evaluation Deceives You?](https://mlsanity.com/paper/2509.22580/) (2025)
- [An Attention-based Feature Memory Design for Energy-Efficient Continual Learning](https://mlsanity.com/paper/2510.04660/) (2025)
- [Action-Adaptive Continual Learning: Enabling Policy Generalization under Dynamic Action Spaces](https://mlsanity.com/paper/2506.05702/) (2025)
- [MAND: Modality-Aware Novelty Detection for Open-World Egocentric Activity Recognition](https://mlsanity.com/paper/2603.16970/) (2026)
- [ProTPS: Prototype-Guided Text Prompt Selection for Continual Learning](https://mlsanity.com/paper/2604.01116/) (2026)

## Abstract (arXiv)

This paper studies the problem of continual learning in an open-world scenario, referred to as Open-world Continual Learning (OwCL). OwCL is increasingly rising while it is highly challenging in two-fold: i) learning a sequence of tasks without forgetting knowns in the past, and ii) identifying unknowns (novel objects/classes) in the future. Existing OwCL methods suffer from the adaptability of task-aware boundaries between knowns and unknowns, and do not consider the mechanism of knowledge transfer. In this work, we propose Pro-KT, a novel prompt-enhanced knowledge transfer model for OwCL. Pro-KT includes two key components: (1) a prompt bank to encode and transfer both task-generic and task-specific knowledge, and (2) a task-aware open-set boundary to identify unknowns in the new tasks. Experimental results using two real-world datasets demonstrate that the proposed Pro-KT outperforms the state-of-the-art counterparts in both the detection of unknowns and the classification of knowns markedly.