mlsanityData to 14 Oct 2026

INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning

arXiv 2401.11667 ↗First posted 22 Jan 2024cs.LGcited by 3

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Abstract (arXiv)

This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting. INCPrompt's key innovation lies in its use of adaptive key-learner and task-aware prompts that capture task-relevant information. This unique combination encapsulates general knowledge across tasks and encodes task-specific knowledge. Our comprehensive evaluation across multiple continual learning benchmarks demonstrates INCPrompt's superiority over existing algorithms, showing its effectiveness in mitigating catastrophic forgetting while maintaining high performance. These results highlight the significant impact of task-aware incremental prompting on continual learning performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 22 Jan 2024.

Builds on

15 of 20 references.

References · 15

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  4. 2016Learning without Forgettingcited by 2,496
  5. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  6. 2017Gradient Episodic Memory for Continual Learningcited by 1,898
  7. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,850
  8. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  9. 2021Learning to Prompt for Continual Learningcited by 944
  10. 2020Dark Experience for General Continual Learning: a Strong, Simple Baselinecited by 911
  11. 2019Large Scale Incremental Learningcited by 732
  12. 2022DualPrompt: Complementary Prompting for Rehearsal-free Continual Learningcited by 642
3 more
  1. 2021Co2L: Contrastive Continual Learningcited by 244
  2. 2023FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformercited by 23
  3. 2023Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learningcited by 11

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.