Continual Distillation Learning


The University of Texas at Dallas
arXiv, 2024
Arxiv Code Results-board

Abstract

We study the problem of Continual Distillation Learning (CDL) that considers Knowledge Distillation (KD) in the Continual Learning (CL) setup. A teacher model and a student model need to learn a sequence of tasks, and the knowledge of the teacher model will be distillated to the student in order to improve the student model. We introduce a novel method named CDL-Prompt that leverages prompt- based continual learning models to build the teacher-student model. We investigate how to utilize the prompts of the teacher model in the student model for knowledge distillation, and propose an attention-based prompt mapping scheme to use the teacher prompts for the student. We demonstrate that our method can be applied to different prompt-based continual learning models such as L2P, DualPrompt and CODA-Prompt to improve their performance using powerful teacher models. While recent CL methods focus on prompt learning, we show that our method can be utilized to build efficient CL models using prompt-based knowledge distillation.

CDL-Prompt

CDL-Prompt is a framework for continual distillation learning that can be integrated into various prompt-based methods to improve performance.

gto

Experiment Results

CDL-Prompt(Using CODA baseline and ViT-base backbone) outperforms other prompt-based methods in both Cifar-100 and ImageNet-R datasets. Accuracy refers to the average accuracy for all 10 tasks. We train multiple times and take the average.

CDL-Prompt(Using CODA baseline and ViT-base backbone) outperforms other prompt-based methods in both Cifar-100 and ImageNet-R datasets. Accuracy refers to the average accuracy for all 10 tasks. We train multiple times and take the average.

 More Prompt-based Models

Adding more methods: Our CDL-Prompt not only uses CODA-Prompt as the baseline model but also includes result statistics from various different prompt-based methods. In the future, we will continuously add and update our CDL method on different prompt-based models.
Cifar-100
# Teacher Student Baseline Task-Number Accuarcy(%) Forgetting(%)
ImageNet-R
# Teacher Student Baseline Task-Number Accuarcy(%) Forgetting(%)

References

Official Code: Source code from the authors of the method

    Code (coming soon...)

    CDL

    The code for CDL-Prompt.

    BibTeX

    Please cite CDL if it helps your research: 
    @misc{2024CDL,
title={Continual Distillation Learning},
author={Qifan Zhang and Yunhui Guo and Yu Xiang},
year={2024},
eprint={2407.13911},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

    Contact

    Send any comments or questions to Qifan Zhang: qifan.zhang@utdallas.edu

    Acknowledgements

    This work was supported in part by the DARPA Perceptually-enabled Task Guidance (PTG) Program under contract number HR00112220005 and the Sony Research Award Program.