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	---
	license: mit
	datasets:
	- uoft-cs/cifar100
	- uoft-cs/cifar10
	- zh-plus/tiny-imagenet
	language:
	- en
	metrics:
	- accuracy
	base_model:
	- microsoft/resnet-18
	pipeline_tag: image-classification
	---


	<div align="center">

	# IDER: Idempotent Experience Replay for Reliable Continual Learning

	[![arXiv](https://img.shields.io/badge/arXiv-2603.00624-b31b1b.svg)](https://arxiv.org/abs/2603.00624)
	[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
	[![Contact Email](https://img.shields.io/badge/Contact-Email-yellow.svg)](https://mail.google.com/mail/?view=cm&fs=1&to=zhanwnagliu@gmail.com)
	<!--<a href="PROJECT_PAGE_LINK"><img src="https://img.shields.io/badge/Project-Page-green.svg" alt="Project Page"></a>-->

	IDER is a novel framework for continual learning based on the idempotent property, which mitigates catastrophic forgetting and improves prediction reliability. It is a simple and robust method that can be easily integrated into other state-of-the-art approaches.

	<hr/>

	<p>
	Zhanwang Liu<sup>1</sup><sup>*</sup>,
	Yuting Li<sup>1</sup><sup>*‡</sup>,
	Haoyuan Gao<sup>1</sup>,
	Yexin Li<sup>4</sup>,
	Linghe Kong<sup>1</sup>,
	Lichao Sun<sup>3</sup>,
	Weiran Huang<sup>1,2</sup><sup>†</sup>
	</p>

	<p>
	<sup>1</sup> School of Computer Science, Shanghai Jiao Tong University
	<sup>2</sup> Shanghai Innovation Institute<br/>
	<sup>3</sup> Lehigh University
	<sup>4</sup> State Key Laboratory of General Artificial Intelligence, BIGAI
	</p>


	<p>
	<sup>*</sup> Equal contribution.
	<sup>†</sup> Corresponding author.
	<sup>‡</sup> Project lead.
	</p>

	<br/>


	</div>

	## 🎉 News
	- [x] [2026.01.26] Our paper has been accepted by ICLR 2026!
	## Table of Content

	* [1. Quick Start](#1-quick-start)
	* [2. Reproduced Results](#2-reproduced-results)
	* [3. Tools](#3-tools)
	* [4. Citation](#4-citation)
	* [5. Acknowledgement](#5-acknowledgement)


	## 1. Quick Start

	### 1.1. Environment



	Clone this repository and install the requirements. Our model can be learnt in a single GPU RTX-4090 24G

	```bash
	conda env create -f environment.yaml
	conda activate icl
	```
	The code was tested on Python 3.10 and PyTorch 1.13.0.


	### 1.2. Training

	Train and evaluate ResNet18 on different datasets using ER and ER+ID with different buffers. Run the following command:

	<details>
	<summary><b>CIFAR-10</b></summary>

	```bash
	bash run_para_cifar10.sh
	```
	</details>
	<details> <summary><b>CIFAR-100</b></summary>

	```bash
	bash run_para_cifar100.sh
	```
	</details>
	<details> <summary><b>TinyImageNet</b></summary>

	```bash
	bash run_para_tinyimg.sh
	```
	</details>

	## 2. Reproduced Results
	The example results are ResNet18 on different datasets using ER and ER+ID as baseline methods with different buffers and 0-4 seeds. All results reported here were obtained by running experiments on an NVIDIA GeForce RTX 4090.
	\| Dataset \| Buffer \| Method \| Forgetting(⬇️) \| TIL(⬆️) \| CIL(⬆️) \| Checkpoint \|
	\|---\|---:\|---\|---:\|---:\|---:\|---\|
	\| CIFAR-10 \| 200 \| ER \| 59.71 ± 2.62 \| 91.48 ± 0.93 \| 48.89 ± 2.19 \| - \|
	\| \| \| ER+ID \| 16.89 ± 2.26 \| 95.87 ± 0.36 \| 70.68 ± 1.10 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-cifar10/buffer_200/erid_seed_0.pth) \|
	\| \| 500 \| ER \| 44.75 ± 2.94 \| 93.38 ± 0.36 \| 60.62 ± 2.46 \| - \|
	\| \| \| ER+ID \| 11.59 ± 2.13 \| 96.20 ± 0.40 \| 75.52 ± 1.35 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-cifar10/buffer_500/erid_seed_0.pth) \|
	\| CIFAR‑100 \| 500 \| ER \| 73.81 ± 0.42 \| 73.98 ± 1.15 \| 21.28 ± 1.08 \| - \|
	\| \| \| ER+ID \| 32.27 ± 1.96 \| 83.30 ± 0.41 \| 45.21 ± 1.20 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-cifar100/buffer_500/erid_seed_0.pth) \|
	\| \| 2000 \| ER \| 54.52 ± 0.62 \| 81.62 ± 0.95 \| 37.93 ± 0.76 \| - \|
	\| \| \| ER+ID \| 18.76 ± 1.52 \| 86.54 ± 0.34 \| 56.30 ± 0.50 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-cifar100/buffer_2000/erid_seed_0.pth) \|
	\| Tiny‑ImageNet \| 4000 \| ER \| 56.89 ± 0.74 \| 66.68 ± 0.47 \| 25.20 ± 0.70 \| - \|
	\| \| \| ER+ID \| 21.62 ± 1.67 \| 74.56 ± 0.55 \| 43.25 ± 1.26 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-tinyimg/buffer_4000/erid_seed_0.pth) \|

	The results below were obtained using an Ascend 910B.
	<details> <summary><b>ASCEND</b></summary>

	\| Dataset \| Buffer \| Method \| Forgetting(⬇️) \| TIL(⬆️) \| CIL(⬆️) \| Checkpoint \|
	\|---\|---:\|---\|---:\|---:\|---:\|---\|
	\| CIFAR-10 \| 200 \| ER+ID \| 16.57 ± 3.29 \| 95.73 ± 0.30 \| 70.85 ± 0.81 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-cifar10/buffer_200/erid_seed_0.pth) \|
	\| \| 500 \| ER+ID \| 12.02 ± 1.39 \| 96.07 ± 0.19 \| 75.06 ± 0.95 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-cifar10/buffer_500/erid_seed_0.pth) \|
	\| CIFAR‑100 \| 500 \| ER+ID \| 31.85 ± 3.50 \| 83.45 ± 0.37 \| 45.55 ± 0.66 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-cifar100/buffer_500/erid_seed_0.pth) \|
	\| \| 2000 \| ER+ID \| 18.99 ± 1.09 \| 86.79 ± 0.30 \| 56.15 ± 0.31 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-cifar100/buffer_2000/erid_seed_0.pth) \|
	\| Tiny‑ImageNet \| 4000 \| ER+ID \| 20.73 ± 0.72 \| 74.30 ± 0.97 \| 43.15 ± 1.20 \| [pth](https://github.com/YutingLi0606/Idempotent-Continual-Learning/tree/main/experiments/seq-tinyimg/buffer_4000/erid_seed_0.pth) \|
	</details>


	The checkpoints are saved under experiments folder.


	## 3. Tools
	<details>
	<summary><b>mlflow visulization</b></summary>

	1. Setup

	```bash
	pip install mlflow
	```

	2. All results are stored in mlflow under the repository mlruns. You can run mlflow ui server locally:
	```bash
	mlflow ui
	```
	And then go to http://127.0.0.1:5000/#/ in your brower to see all the results from the experiments we runned and exact hyperparameters used in each run.

	</details>


	## 4. Citation
	If our project is helpful for your research, please consider citing :

	```
	@article{liu2026ider,
	title={IDER: IDempotent Experience Replay for Reliable Continual Learning},
	author={Liu, Zhanwang and Li, Yuting and Gao, Haoyuan and Li, Yexin and Kong, Linghe and Sun, Lichao and Huang, Weiran},
	journal={arXiv preprint arXiv:2603.00624},
	year={2026}
	}
	```


	## 5. Acknowledgement
	Supported by the Shanghai Municipal Special Program for Basic Research on General AI Foundation Models (Grant No. 2025SHZDZX025G03) and the SJTU Kunpeng \& Ascend Center of Excellence.

	This project is heavily based on [Mammoth](https://github.com/aimagelab/mammoth) and [weight-interpolation-cl](https://github.com/jedrzejkozal/weight-interpolation-cl). We sincerely appreciate the authors of the mentioned works for sharing such great library as open-source project.

	✨ Feel free to contribute and reach out if you have any questions! ✨
	📧 Email: [zhanwnagliu@gmail.com](mailto:zhanwnagliu@gmail.com)