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Repo for Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective (TPAMI2026)

NTK-CL 최신버젼 다운로드

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# Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective

[![arXiv](https://img.shields.io/badge/arXiv-2407.17120-b31b1b.svg)](https://arxiv.org/abs/2407.17120)
[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://raw.githubusercontent.com/Programmergg/NTK-CL/main/)
[![Python](https://img.shields.io/badge/Python-3.11%2B-blue.svg)](https://raw.githubusercontent.com/Programmergg/NTK-CL/main/)

Jingren Liu, Zhong Ji, Yunlong Yu, Jiale Cao, Yanwei Pang, Jungong Han, Xuelong Li
</div>

---

## 🔥 News
- **[2026-03-08]** Code and scripts released.
- **[2026-03-08]** Reproducibility guide updated.

---

## 🧩 Overview
This repository provides the official implementation for:

**Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective**.

We study PEFT-CL via **Neural Tangent Kernel (NTK)** theory and introduce **NTK-CL**, a framework that:
- avoids task-specific parameter storage,
- adaptively generates task-relevant features,
- reduces task-interplay and task-specific generalization gaps,
- achieves strong performance on established PEFT-CL benchmarks.

---

## ✅ Key Features
- [x] Training / evaluation scripts for PEFT-CL benchmarks
- [x] NTK-CL implementation
- [x] Dataset preparation scripts
---

## 🗂️ Repository Structure

```text
.
├── data/                       # datasets / prepared data
├── dataloader/                 # dataset & dataloader implementations
├── models/                     # model definitions / PEFT modules / backbones
├── trainer.py                  # training loop / continual learning pipeline
├── requirements.txt            # python dependencies for reproducing the environment
└── main.py                     # main entry for training/evaluation
```
---

## 🧰 Installation

### 1) Create environment
```bash
conda create -n ntk_cl python=3.11 -y
conda activate ntk_cl
```

### 2) Install dependencies
```bash
pip install -r requirements.txt
```
---

## 📦 Datasets

To facilitate reproduction, we provide downloadable dataset packages/links for the benchmarks used in this project, including:
CIFAR-100, ImageNet-R, ImageNet-A, DomainNet, Oxford Pets, EuroSAT, PlantVillage, VTAB ,and Kvasir.

### Download
You can download the datasets from the following links:

- **Baidu Netdisk**: `https://pan.baidu.com/s/1PwDAytZY5sKyUInecc5dkg?pwd=arrd`

> Please download the required datasets and place them under the `data/` directory following the structure expected by the code.

### Directory Structure
A recommended dataset layout is:

```text
data/
├── cifar-100-python/
├── imagenet-r/
├── imagenet-a/
├── DomainNet/
├── OxfordPets/
├── EuroSAT/
├── PlantVillage/
├── VTAB/
└── Kvasir/
```

---

## 🚀 Quick Start

### Train and Evaluate
**Example: replace args with your actual argparse options**
```bash
python main.py \
  --dataset cifar224_in21k \
  --suffix cifar224_in21k \
  --model_name both \
  --seed [0, 1, 2, 3, 4] \
  --device 0
```

---

## 📈 Results
We present the main experimental results of **NTK-CL** below.

---

## 🧾 Citation

If you find this repository useful, please cite our paper:

```bibtex
@article{liu2024parameter,
  title={Parameter-efficient fine-tuning for continual learning: A neural tangent kernel perspective},
  author={Liu, Jingren and Ji, Zhong and Yu, YunLong and Cao, Jiale and Pang, Yanwei and Han, Jungong and Li, Xuelong},
  journal={arXiv preprint arXiv:2407.17120},
  year={2024}
}
```

---

## ⭐ Star History

---

## 📬 Contact

For questions and discussions:
- Email: **jrl0219@tju.edu.cn**
- Issues: please open a GitHub Issue in this repo.

---

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