README.md

metadata

license: mit
pipeline_tag: image-classification
tags:
  - continual-learning
  - meta-learning
  - computer-vision

MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning

This repository contains the model checkpoints and artifacts for MePo (Meta Post-Refinement), as presented in the paper MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning.

Introduction

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

• Paper: MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning
• GitHub Repository: SunGL001/MePo

Methodology Overview

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.

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:

@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}, 
}