| --- |
| license: mit |
| pipeline_tag: image-classification |
| tags: |
| - continual-learning |
| - meta-learning |
| - computer-vision |
| --- |
| |
| # MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning |
|
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| This repository contains the model checkpoints and artifacts for **MePo** (**Me**ta **Po**st-Refinement), as presented in the paper [MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning](https://huggingface.co/papers/2602.07940). |
|
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| ## Introduction |
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| **MePo** is an innovative approach for Pretrained Models (PTMs)-based General Continual Learning (GCL). It addresses the challenges of online datastreams and blurry task boundaries by refining the pretrained backbone before downstream continual learning. Inspired by meta-plasticity and reconstructive memory in neuroscience, MePo adapts representation learning to facilitate rapid adaptation to evolving environments in a rehearsal-free manner. |
|
|
| ## Resources |
|
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| - **Paper:** [MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning](https://huggingface.co/papers/2602.07940) |
| - **GitHub Repository:** [SunGL001/MePo](https://github.com/SunGL001/MePo) |
|
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| ## Methodology Overview |
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| MePo improves GCL performance through three core components: |
| 1. **Meta-Learning for Representation Learning**: A bi-level meta-learning paradigm that constructs pseudo task sequences from pretraining data to refine the backbone. |
| 2. **Meta Covariance Initialization**: Initializes a meta covariance matrix as the reference geometry of the pretrained representation space, enabling the exploitation of second-order statistics. |
| 3. **Feature Alignment**: Ensures robust output alignment during downstream tasks. |
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| The method serves as a plug-in strategy that achieves significant gains across various benchmarks such as CIFAR-100, ImageNet-R, and CUB-200. |
|
|
| ## Citation |
|
|
| If you find this work useful for your research, please cite: |
|
|
| ```bibtex |
| @misc{sun2026mepometapostrefinementrehearsalfree, |
| title={MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learnin}, |
| author={Guanglong Sun and Hongwei Yan and Liyuan Wang and Zhiqi Kang and Shuang Cui and Hang Su and Jun Zhu and Yi Zhong}, |
| year={2026}, |
| eprint={2602.07940}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2602.07940}, |
| } |
| ``` |
